Establishment and Validation for the Theoretical Model of the Vehicle Airbag
肺结节良恶性概率预测模型的建立与验证
㊃肿瘤专栏㊃[收稿日期]2023-03-17[基金项目]广安市肺结节/肺癌全程管理研究(2020S Y F 03)[作者简介]黄语嫣(1994-),女,四川德阳人,川北医学院附属医院医学硕士研究生,从事呼吸肿瘤诊治研究㊂*通信作者㊂E -m a i l :l a n q i l i l y@163.c o m 肺结节良恶性概率预测模型的建立与验证黄语嫣1,邓太兵2,庞 敏1,姜永杰1,蒋 莉1*(1.川北医学院附属医院呼吸与危重症医学科,四川南充637000;2.四川省广安市人民医院呼吸与危重症医学,四川广安638001) [摘要] 目的通过分析肺结节的临床特征和影像学表现,筛选影响肺结节良恶性的因素,并建立及验证预测模型,为肺结节良恶性的鉴别提供参考依据㊂方法选取肺结节患者1160例为研究对象,所有患者按2ʒ1随机分为2组,建模组773例,验证组387例㊂建模组数据进行单因素分析,差异有统计学意义的变量纳入二元L o g i s t i c 回归分析,获得肺结节良恶性病变的独立预测因子,建立良恶性概率预测模型㊂验证并比较本研究模型与传统经典模型M a yo 模型㊁B r o c k 模型㊁V A 模型㊁北京大学人民医院模型之间的预测性能㊂结果性别㊁年龄㊁肺癌家族史㊁结节质地㊁结节直径㊁分叶征㊁空泡征㊁血管集束征㊁钙化征㊁细支气管征㊁胸膜牵拉征为肺结节良恶性病变的独立预测因子(P <0.05)㊂本研究模型的受试者工作特征曲线下面积(0.856)高于M a y o 模型(0.604)㊁B r o c k 模型(0.447)㊁V A 模型(0.569)及北京大学人民医院模型(0.677),其预测敏感度为86.10%,特异度为73.70%㊂结论本研究构建的预测模型具有良好的诊断效能,可能优于传统经典模型,对临床医生鉴别肺结节的良恶性有一定的参考价值㊂[关键词] 多发性肺结节;肺肿瘤;预测模型 d o i :10.3969/j .i s s n .1007-3205.2024.03.005 [中图分类号] R 734.2 [文献标志码] A [文章编号] 1007-3205(2024)03-0278-06E s t a b l i s h m e n t a n dv a l i d a t i o no f p r o b a b i l i t yp r e d i c t i o nm o d e l f o r b e n i gn a n dm a l i g n a n t p u l m o n a r y no d u l e s HU A N G Y u -y a n 1,D E N G T a i -b i n g 2,P A N G M i n 1,J I A N G Y o n g -ji e 1,J I A N GL i 1*(1.D e p a r t m e n t o f R e s p i r a t o r y a n dC r i t i c a lC a r eM e d i c i n e ,t h eA f f i l i a t e d H o s p i t a l o f No r t hS i c h u a n M e d i c a lC o l l e g e ,N a n c h o n g 637000,C h i n a ;2.D e p a r t m e n t o f R e s p i r a t o r y an d C r i t i c a lC a r eM e d i c i n e ,G u a n g 'a nP e o p l e 'sH o s pi t a l ,S i c h u a nP r o v i n c e ,G u a n g'a n 638001,C h i n a )[A b s t r a c t ] O b j e c t i v e T oa n a l y z e t h e c l i n i c a l c h a r a c t e r i s t i c s a n d i m a g i n g m a n i f e s t a t i o n so f p u l m o n a r y n o d u l e s ,t o s c r e e n t h e f a c t o r s t h a t a f f e c t t h em a l i g n a n c y o f p u l m o n a r y no d u l e s ,a n d t o e s t a b l i s ha n dv a l i d a t e p r e d i c t i o nm o d e l s ,t h u s p r o v i d i n g a r e f e r e n c e b a s i s f o r t h e d i f f e r e n t i a t i o n o f b e n i g na n d m a l i g n a n t p u l m o n a r y n o d u l e s .M e t h o d s At o t a lo f1160p a t i e n t sw i t h p u l m o n a r y n o d u l e sw e r e s e l e c t e da s t h e r e s e a r c hs u b j e c t s ,a n dt h e nr a n d o m l y d i v i d e d i n t o t w o g r o u psa t a r a t i oo f 2ʒ1:am o d e l i n gg r o u p (n =773)a n dav a l i d a t i o n g r o u p (n =387).U n i v a r i a t ea n a l ys i s w a sc o n d u c t e d o n t h e d a t a o f m o d e l i n g g r o u p ,a n d v a r i a b l e s w i t h s t a t i s t i c a l l y s i g n i f i c a n t d i f f e r e n c e sw e r e i n c l u d e di nb i n a r y l o g i s t i cr e g r e s s i o na n a l y s i st oo b t a i ni n d e pe n d e n t p r e d i c t i v ef a c t o r s f o rb e n ig na n d m a l i g n a n t p u l m o n a r y n o d u l e s ,a n dt oe s t a b l i s ha p r o b a b i l i t ypr e d i c t i o n m o d e l f o r b e n i g n a n dm a l i g n a n t l e s i o n s .T h e p r e d i c t i v e p e r f o r m a n c e o f t h i sm o d e l w i t h t r a d i t i o n a l ㊃872㊃第45卷第3期2024年3月河北医科大学学报J O U R N A L O F H E B E I M E D I C A L U N I V E R S I T YV o l .45 N o .3M a r . 2024c l a s s i cm ode l s s u c ha s M a y o m o d e l,B r o c k m o d e l,V A m o d e l,a n d m o d e l o fP e k i n g U n i v e r s i t y P e o p l e's H o s p i t a lw a sv e r if i e da n dc o m p a r e d.R e s u l t s Ag e,g e n d e r,f a m i l yhi s t o r y o fl u n g c a n c e r,n o d u l e t e x t u r e,n o d u l ed i a m e t e r,l o b u l a t i o ns i g n,v a c u o l a r s i g n,v a s c u l a rb u n d l es i g n, c a l c i f i c a t i o ns i g n,b r o n c h i o l i t i ss i g n,a n d p l e u r a l t r a c t i o ns i g n w e r ei n d e p e n d e n t p r e d i c t o r so fb e n i g na n d m a l i g n a n t p u l m o n a r y n o d u l e s(P<0.05).T h ea r e au n d e rt h er ec e i v e ro p e r a t i n gc h a r a c t e r i s t i c(R O C)c u r v e(A U C)o f t h i sm ode l(0.856)w a s h i g h e r t h a n t h a t of t h eM a y om o d e l(0.604),B r o c km o d e l(0.447),V A m o d e l(0.569),a n d m o d e lo fP e k i n g U n i v e r s i t y P e o p l e'sH o s p i t a l(0.677),w i t ha p r e d i c t i v e s e n s i t i v i t y o f86.10%a n d a s p e c i f i c i t y o f73.70%.C o n c l u s i o n T h e p r e d i c t i o nm o d e l c o n s t r u c t e d i n t h i s s t u d y i sw i t h g o o dd i a g n o s t i c e f f i c a c y,w h i c h m a y b e s u p e r i o r t o t h e t r a d i t i o n a lm o d e l s,w h i c hh a s g r e a t e r r e f e r e n c ev a l u e f o r c l i n i c i a n s t od i s t i n g u i s h t h eb e n i g na n dm a l i g n a n t p u l m o n a r y n o d u l e s.[K e y w o r d s] m u l t i p l e p u l m o n a r y n o d u l e s;l u n g n e o p l a s m s;p r e d i c t i o nm o d e l肺结节是影像学上直径ɤ3c m的局灶㊁密度增高的肺部阴影,性质为实性或亚实性,可以是孤立性或多发性,且无肺不张㊁肺门淋巴结肿大及胸腔积液[1]㊂由于近年来人们对体检的重视及低剂量计算机断层扫描(l o w-d o s e h e l i c a l c o m p u t e d t o m o g r a p h y,L D C T)的广泛应用,肺结节的检出率明显升高㊂研究[2]显示,在北美㊁欧洲和东亚地区,肺结节的发病率分别为23%㊁29%㊁35.5%,恶变率分别为1.7%㊁1.2%㊁0.54%㊂如今,肺癌仍为世界癌症死亡的主要原因㊂肺癌的5年生存率从I A1期至I V B期为92%~100%㊂早期诊断肺癌尤为重要㊂美国肺癌筛查结果显示,L D C T的应用使肺癌病死率相对降低20%㊂但C T筛查可出现大量假阳性结果[3],增加后期随访及治疗的经济负担和精神负担㊂活检为有创检查,不必要情况下需尽量避免㊂因此,运用肺结节良恶性预测模型,在活检之前评估肺癌的概率十分重要㊂基于此,本研究旨在通过分析肺结节的临床特征和影像学表现,筛选影响肺结节良恶性的因素,并建立及验证预测模型,为肺结节良恶性的鉴别提供参考依据㊂1资料与方法1.1一般资料选取2019年1月 2021年11月川北医学院附属医院和广安市人民医院收治的肺结节患者1160例为试验组,其中男性544例,女性616例,平均年龄(56.37ʃ10.56)岁,恶性结节共805例,占69.40%,良性结节共355例,占30.60%㊂纳入标准:①胸部C T示直径介于5~30mm的肺结节病例;②肺部原发,病理诊断明确,恶性可为外科手术和小活检标本,良性为外科手术标本;③病理诊断前半年内在研究中心拍摄过胸部C T㊂排除标准:①完全钙化结节;②临床信息不完整,无法采集数据;③既往肺癌病史㊂本研究通过医院伦理委员会批准㊂所有患者及其家属均知情同意且签署知情同意书㊂1.2数据收集收集患者的临床资料㊁结节的影像学特征㊂临床资料包括性别㊁年龄㊁吸烟史㊁年吸烟量㊁粉尘接触史㊁合并症(慢性阻塞性肺病㊁弥漫性肺纤维化㊁既往肺结核㊁尘肺)㊁既往恶性肿瘤史㊁恶性肿瘤家族史㊁肺癌家族史㊁非肺癌恶性肿瘤家族史㊂影像学特征包括结节质地(实性结节㊁混合磨玻璃结节㊁纯磨玻璃结节)㊁结节直径(mm)㊁结节位置(右肺上㊁中㊁下叶;左肺上㊁下叶)㊁结节形态㊁边缘光滑与否㊁毛刺征㊁分叶征㊁钙化征㊁空洞征㊁空泡征㊁血管集束征㊁细支气管征㊁胸膜牵拉征㊂影像学特征由影像科医生判读结果为准㊂1.3构建及验证模型所有患者按2ʒ1随机分为2组,建模组773例,验证组387例㊂建模组数据进行单因素分析,差异有统计学意义的变量纳入二元L o g i s t i c回归分析,获得肺结节良恶性病变的独立预测因子,建立良恶性概率预测模型㊂绘制受试者工作特征(r e c e i v e ro p e r a t i n g c h a r a c t e r i s t i c,R O C)曲线,计算曲线下面积(a r e au n d e r c u r v e,A U C),确定最佳诊断分界点,计算敏感度(s e n s i t i v i t y,S e)㊁特异度(s p e c i f i c i t y,S p)㊁阳性预测值(p o s i t i v e p r e d i c t i v ev a l u e,P P V)㊁阴性预测值(n e g a t i v e p r e d i c t i v ev a l u e,N P V)㊁正确率(a c c u r a c y,A c c)㊂将验证组代入M a y o模型㊁B r o c k模型㊁V A模型㊁P K U P H模型及本研究模型,绘制R O C曲线,计算不同模型的A U C㊁S e㊁S p㊁P P V㊁N P V㊁A c c,比较模型预测性能㊂1.4统计学方法应用S P S S26.0统计软件分析数据㊂计量资料比较采用t检验和M a n n-W h i t n e y U检验,计数资料比较采用χ2检验,采用二元㊃972㊃河北医科大学学报第45卷第3期L o gi s t i c 回归分析获得预测独立因子,绘制R O C 曲线检测最佳诊断分界点㊂P <0.05为差异有统计学意义㊂2 结 果2.1 建模组的单因素分析 良恶性病变组在性别㊁年龄㊁吸烟史㊁年吸烟量㊁肺癌家族史㊁结节质地㊁结节直径㊁结节边缘和形态㊁分叶征㊁钙化征㊁空泡征㊁血管集束征㊁细支气管征㊁胸膜牵拉征方面的比较差异有统计学意义(P <0.05),见表1㊂表1 建模组肺结节的单因素分析T a b l e 1 U n i v a r i a t e a n a l y s i s o f p u l m o n a r y n o d u l e s i n t h em o d e l i n g g r o u p组别例数性别(例数,%)男性女性年龄(x -ʃs ,岁)吸烟史(例数,%)有无年吸烟量(例数,%)ȡ400支/年<400支/年良性病变组262156(59.5)106(40.5)55.53ʃ10.3089(34.0)173(66.0)86(32.8)176(67.2)恶性病变组511216(42.3)295(57.7)57.78ʃ10.44136(26.6)375(73.4)126(24.7)385(75.3)χ2/t 值20.6962.8594.5405.804P 值<0.0010.0040.0330.016组别例数恶性肿瘤家族史(例数,%)有无肺癌家族史(例数,%)有无非肺癌家族史(例数,%)有无良性病变组2626(2.3)256(97.7)2(0.8)260(99.2)5(1.9)257(98.1)恶性病变组51120(3.9)491(96.1)17(3.3)494(96.7)5(1.0)506(99.0)χ2/t 值1.4054.7470.558P 值0.2360.0290.455组别例数胸膜牵拉征(例数,%)有无质地(例数,%)实性结节混合磨玻璃结节纯磨玻璃结节良性病变组262144(55.0)118(45.0)208(79.4)25(9.5)29(11.1)恶性病变组511334(65.4)177(34.6)300(58.7)156(30.5)55(10.8)χ2/t 值7.93843.864P 值0.005<0.001组别例数位置(例数,%)右上右中右下左上左下形态(例数,%)规则不规则良性病变组26294(35.9)17(6.5)62(23.7)48(18.3)41(15.6)25(9.5)237(90.5)恶性病变组511170(33.3)43(8.4)87(17.0)129(25.2)82(16.0)21(4.1)490(95.9)χ2/t 值8.7779.133P 值0.0670.003组别例数边缘(例数,%)光滑不光滑毛刺征(例数,%)有无细支气管征(例数,%)有无良性病变组26243(16.4)219(83.6)67(25.6)195(74.4)22(8.4)240(91.6)恶性病变组51155(10.8)456(89.2)164(32.1)347(67.9)174(34.1)337(65.9)χ2/t 值4.9923.51560.225P 值0.0250.061<0.001组别例数分叶征(例数,%)有无钙化征(例数,%)有无空洞征(例数,%)有无良性病变组262210(80.2)52(19.8)2(0.4)509(99.6)4(1.5)258(98.5)恶性病变组511442(86.5)69(13.5)16(6.1)246(93.9)13(2.5)498(97.5)χ2/t 值5.28024.8760.833P 值0.022<0.0010.361组别例数空泡征(例数,%)有无血管集束征(例数,%)有无良性病变组26221(8.0) 241(92.0)141(53.8)121(46.2)恶性病变组51170(13.7)441(86.3)442(86.5)69(13.5)χ2/t 值5.38699.782P 值0.020<0.0012.2 多因素分析结果 以肺结节病变良恶性为因变量(良性=0,恶性=1)以单因素分析中差异有统计学意义的变量为自变量(赋值表见表2),纳入二元L o g i s t i c 回归分析,结果显示性别㊁年龄㊁肺癌家族史㊁结节质地㊁结节直径㊁分叶征㊁空泡征㊁血管集束征㊁钙化征㊁细支气管征㊁胸膜牵拉征为肺结节良恶性病变的独立预测因子(P <0.05)㊂结节质地中,混合磨玻璃更易发生恶性病变(P <0.05)㊂见表3㊂㊃082㊃河北医科大学学报 第45卷 第3期表2L o g i s t i c回归分析法赋值T a b l e2L o g i s t i c r e g r e s s i o na n a l y s i s a s s i g n m e n t s变量赋值变量赋值变量赋值年龄连续变量年吸烟量<400支/年=0,ȡ400支/年=1肺癌家族史无=0,有=1性别男性=0,女性=1钙化征无=0,有=1结节直径连续变量吸烟史无=0,有=1质地纯磨玻璃结节=1,混合磨玻璃结节=2,实性结节=3血管集束征无=0,有=1分叶征无=0,有=1形态不规则=0,规则=1胸膜牵拉征无=0,有=1空泡征无=0,有=1边缘不光滑=0,光滑=1细支气管征无=0,有=1表3建模组肺结节的多因素分析T a b l e3M u l t i v a r i a t e a n a l y s i s o f p u l m o n a r y n o d u l e s i n t h em o d e l i n g g r o u p 指标回归系数标准误W a l dχ2值P值O R值95%C I 年龄0.0210.0104.7750.0291.0211.002~1.041性别0.9250.19921.543<0.0012.5211.706~3.725肺癌家族史1.6570.8114.1780.0415.2441.071~25.686结节质地纯磨玻璃结节0.8550.3147.4300.0062.3511.271~4.345混合性磨玻璃结节2.0800.30247.534<0.0018.0074.432~14.464结节直径0.0600.01613.144<0.0011.0611.028~1.096分叶征0.5690.2804.1360.0421.7661.021~3.054钙化征-4.2000.99917.676<0.0010.0150.002~0.106空泡征1.0930.32511.3240.0012.9821.578~5.634血管集束征1.9510.22475.785<0.0017.0374.535~10.918细支气管征1.6880.27437.862<0.0015.4113.160~9.265胸膜牵拉征0.5150.2175.6270.0181.6741.094~2.563 2.3构建和评价临床预测模型预测模型使用以下公式计算恶性概率:恶性概率(P)=e x/(1+e x),x=-4.890+(0.021ˑ年龄)+(0.925ˑ性别)+(1.657ˑ肺癌家族史)+(0.855ˑ纯磨玻璃结节)+(2.080ˑ混合磨玻璃结)+(0.515ˑ胸膜牵拉征)+(0.569ˑ分叶征)+(0.06ˑ结节直径+1.093ˑ空泡征-4.2ˑ钙化征+1.951ˑ血管集束征+1.688ˑ细支气管征)㊂其中,e为自然对数,X方程中,性别为女,取值为1,性别为男,取值为0;对于肺癌家族史㊁纯磨玻璃结节㊁混合磨玻璃结节㊁血管集束征㊁钙化征㊁空泡征㊁细支气管征㊁胸膜牵拉征㊁分叶征,有对应元素取值为1,否则为0㊂绘制建模组R O C曲线(图1),其A U C为0.852(95%C I:0.823~0.881)㊂模型最佳诊断分界点为0.667,S e为78.5%,S p为80.0%,P P V为87.0%,N P V 为78.0%,A c c为78.0%㊂2.4模型的验证及比较将验证组数据代入M a y o 模型㊁B r o c k模型㊁V A模型㊁P K U P H模型及本研究模型,绘制R O C曲线(图2),比较各模型的A U C㊁S e㊁S p㊁P P V㊁N P V㊁A c c,结果显示本研究模型A U C 明显高于其余4种模型,其预测敏感度为86.10%,特异度为73.70%,见表4㊂图1建模组的R O C曲线F i g u r e1R O Cc u r v e o f t h em o d e l i n g g r o u p图2验证组各模型的R O C曲线F i g u r e2R O Cc u r v e o f e a c hm o d e l i n t h e v a l i d a t i o n g r o u p㊃182㊃河北医科大学学报第45卷第3期表4 验证组各模型的预测性能T a b l e 4 P r e d i c t i o n p e r f o r m a n c e o f e a c hm o d e l i n t h e v a l i d a t i o n g r o u p模型A U CS e (%)S p (%)P P V (%)P V 值(%)A C C (%)95%C IM a yo 0.60451.4080.9082.0032.0056.000.541~0.666B r o c k 0.44710.4090.9074.000.0074.000.381~0.514P K U P H 0.67763.9064.6084.0038.0064.000.615~0.738V A 0.56968.1047.5079.0034.0063.000.503~0.634本研究0.85686.1073.7091.0065.0083.000.811~0.9013 讨 论M a yo 模型㊁B r o c k 模型㊁V A 模型㊁P K U P H 模型均在国内群体验证过且是最被感兴趣的国内外经典良恶性概率预测模型[4]㊂但M a yo 模型的影像学资料来源于胸部X 线片,约12%的肺结节无确切的良恶性诊断结果[5],可能导致模型的准确性欠佳㊂B r o c k 模型纳入的患者年龄50~75岁,均有吸烟史,且排除了既往肿瘤史患者[6],模型的适用人群范围可能较窄㊂V A 模型研究主体人群为老年白人男性,且90%以上的患者有吸烟史[7],导致模型在女性患者中应用受限㊂前3个模型均是基于非亚洲人群建立的,亚太结节评估指南指出,在非亚洲人群中开发的评估模型可能存在不适用性㊂P K U P H 模型基于国内人群建立,在国内应用较广泛,但该模型排除了5年内有恶性肿瘤史的患者[8],使得其运用范围受到一定的限制㊂而本研究分析了2个中心共1160例肺结节患者患者多达23项临床数据和影像学特征,所有病例都有完整的影像学数据及明确的病理诊断,经过单因素㊁多因素分析,筛选了11项肺结节良恶性独立预测因子,构建了新的预测模型,用于评估肺结节的恶性风险,验证并比较了新模型与M a yo 模型㊁B r o c k 模型㊁V A 模型㊁P K U P H 模型之间的预测性能,结果显示本模型的A U C 大于经典模型,证明本研究模型预测肺结节良恶性能力可能优于现有的4个模型,对于良恶性肺结节的鉴别可能有更高的临床价值㊂与上述4个经典预测模型一样,本研究模型中也包含性别㊁年龄㊁结节直径㊁钙化征,另外,本研究模型新增了结节质地(包括纯磨玻璃和混合磨玻璃)㊁血管集束征㊁空泡征㊁细支气管征㊁胸膜牵拉征㊁分叶征和肺癌家族史㊂M c W i l l i a m s 等[6]对1871例包括7008个肺结节的研究显示,混合磨玻璃结节类型是癌症的预测因素㊂本研究结果也显示结节质地是肺结节良恶性的危险因素,并且混合磨玻璃结节恶性风险更高㊂血管征是恶性肿瘤的重要指标,肿瘤的生长和转移依赖于新生血管㊂血管集束征在良性结节中占31%,恶性结节中占54%㊂Z h o u 等[9]建立的孤立性肺结节恶性肿瘤风险临床模型中,纳入血管集束征为危险因素㊂本研究结果与既往研究结果一致,认为有血管集束征的肺结节恶性风险更大㊂肺结节中直径<5mm 的低密度区为空泡征,而低密度区连续多个层面,直达结节外缘为细支气管征㊂本研究结果显示空泡征和细支气管征为恶性肺结节的危险因素,与既往研究结果一致㊂X i a 等[10]建立肺癌预测模型中包括了空泡征和细支气管征,Z h a o 等[11]也将细支气管征纳入预测模型,H o u 等[12]认为空泡征的存在与恶性结节相关㊂本研究模型中还包括胸膜牵拉征和分叶征,X i a 等[10]研究显示,胸膜牵拉征和分叶征均为恶性肺结节的独立预测因素,Z h a o 等[11]同样在肺结节的良恶性预测模型中纳入了胸膜牵拉征,Y u 等[13]开发的孤立性肺结节恶性预测模型中也包括了分叶征㊂肺癌家族史与肺癌风险相关并被用作肺癌风险的预测因子[14],M c W i l l i a m s 等[6]研究中建立的完整模型也将肺癌家族史归为癌症的预测因素㊂本研究模型中不包括毛刺征㊁上叶位置和吸烟史㊂毛刺征也可出现于良性病变中㊂荟萃分析[15]显示周围型肺癌和炎性假瘤都可有毛刺征,但肺癌的毛刺短而细,炎性假瘤的毛刺长而粗㊂W e i 等[16]研究表明肺结核也有毛刺表现,王新强等[17]在错构瘤中显示毛刺征,X i a o 等[18]报道了肺部炎性结节中有毛刺征㊂本研究单因素分析显示毛刺征在肺结节良恶性方面比较差异无统计学意义,可能与本研究中肺结核㊁炎性假瘤及炎性结节占比较大相关㊂对于上叶位置,其是我国肺结核好发部位,因此对于位于上叶位置的肺结节恶性概率大的说法并不完全适合我国[1]㊂本研究中腺癌比率明显高于鳞癌,与既往研究[4]一致㊂近年来,可能由于空气污染等原因,非吸烟肺癌患者比率逐年升高,且女性腺癌发病率较高㊂有对中国不同地区6家医院的8392名员工的体检资料的研究,结果也显示肺癌患者中非吸烟者多于吸烟者[19]㊂综上所述,本研究筛选出性别㊁年龄㊁肺癌家族史㊁结节质地㊁结节直径㊁分叶征㊁空泡征㊁血管集束㊃282㊃河北医科大学学报 第45卷 第3期征㊁钙化征㊁细支气管征㊁胸膜牵拉征是判断肺结节良恶性病变的独立预测因子㊂结节质地中,磨玻璃结节发生恶性病变的风险高于实性结节,且混合磨玻璃结节更容易发生恶性病变㊂本研究建立的预测模型区分度优于传统经典模型,但本研究模型在其他人群中的表现是否也优于传统模型,还需要更多研究验证㊂[参考文献][1]中华医学会呼吸病学分会肺癌学组.中国肺癌防治联盟专家组.肺结节诊治中国专家共识(2018年版)[J].中华结核和呼吸杂志,2018,41(10):763-771.[2]S u n g H,F e r l a y J,S i e g e lR L,e ta l.G l o b a lc a n c e rs t a t i s t i c s2020:G L O B O C A N e s t i m a t e s o fi n c i d e n c e a n d m o r t a l i t yw o r l d w i d e f o r36c a n c e r s i n185c o u n t r i e s[J].C A C a n c e rJC l i n,2021,71(3):209-249.[3] H e X,X u e N,L i u X,e t a l.A n o v e lc l i n i c a l m o d e lf o rp r e d i c t i n g m a l i g n a n c y o f s o l i t a r y p u l m o n a r y n o d u l e s:am 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a n c e rb a s e d o nr a d i o m i c sa n d C Tf e a t u r e s[J].T r a n s l C a n c e rR e s,2021,10(10):4454-4463.[17]王新强,王永广,李军.肺纤维平滑肌瘤型错构瘤C T表现与病理对照分析[J].国际医药卫生导报,2020,26(5):714-716.[18] X i a o Y D,L v F J,L i W J,e t a l.S o l i t a r y p u l m o n a r yi n f l a mm a t o r y n o d u l e:c t f e a t u r e s a n d p a t h o l o g i c a l f i n d i n g s[J].J I n f l a mm R e s,2021,14:2741-2751.[19] Z h a n g Y,J h e o nS,L iH,e ta l.R e s u l t so f l o w-d o s ec o m p u t e dt o m o g r a p h y a sar e g u l a rh e a l t he x a m i n a t i o na m o n g C h i n e s eh o s p i t a l e m p l o y e e s[J].JT h o r a cC a r d i o v a s cS u r g,2020,160(3):824-831.e4.(本文编辑:何祯)㊃382㊃黄语嫣等肺结节良恶性概率预测模型的建立与验证。
进展期胃癌脉管侵犯术前列线图预测模型的建立和验证
临床研究进展期胃癌脉管侵犯术前列线图预测模型的建立和验证郭振江，赵光远，杜立强，刘防震△摘要：目的探讨进展期胃癌脉管侵犯（LVI）的术前预测因素，建立对应的列线图预测模型并进行内部验证。
方法选取经胃肠外科手术切除的进展期胃癌246例。
根据术后病理诊断将患者分为LVI阳性组（95例）和LVI阴性组（151例）。
收集患者年龄、性别、肿瘤分化、肿瘤大小、肿瘤部位、Borrmann分型、Lauren分型、cT分期、cN分期、系统性免疫炎症指数（SII），并比较2组间上述基线及临床特征的差异，将2组间差异有统计学意义的变量进行多因素Logistic回归，并进一步建立可视化列线图预测模型，运用Bootstrap法对模型预测效能进行内部验证。
结果2组间肿瘤大小、Borrmann分型、肿瘤分化、Lauren分型、cT分期、cN分期及SII差异均有统计学意义（P＜0.05）。
多因素Logistic回归分析结果显示肿瘤大小（OR=2.184，95%CI：1.224~3.898）、Borrmann分型（OR=2.517，95%CI：1.294~ 4.896）、cT分期（OR=1.860，95%CI：1.045~3.308）、cN分期（OR=1.816，95%CI：1.004~3.285）及SII（OR=1.001，95%CI：1.000~1.002）是进展期胃癌LVI的独立预测因素，依据多因素分析结果建立进展期胃癌LVI的术前列线图预测模型。
经内部验证，列线图模型受试者工作特征（ROC）曲线的曲线下面积（AUC）为0.735，分别高于肿瘤大小（0.599）、Borrmann分型（0.564）、cT分期（0.604）、cN分期（0.582）和SII（0.615）。
校准曲线显示模型预测LVI的概率与实际发生的概率具有较好一致性。
Hosmer-Lemeshow检验显示拟合优度良好（χ2=4.387，P=0.821）。
结论建立的列线图预测模型有助于术前预测进展期胃癌LVI发生的概率，可为临床个体化治疗提供指导。
伏立康唑治疗对肺部侵袭性真菌感染患者肺功能的影响
伏立康唑治疗对肺部侵袭性真菌感染患者肺功能的影响胥亚，孙杨，胥琳琳盐城市第一人民医院检验科，江苏盐城224000[摘要]目的分析伏立康唑治疗对肺部侵袭性真菌感染（invasive pulmonary fungal infection，IPFI）患者肺功能的影响。
方法选取2021年7月—2022年12月盐城市第一人民医院收治的60例肺部IPFI患者为研究对象，按照信封法分为两组，对照组（n=30）给予常规治疗，观察组（n=30）另加用伏立康唑治疗，对比两组治疗效果、肺功能、炎症因子及不良反应发生情况。
结果观察组治疗有效率96.67%高于对照组的73.33%，差异有统计学意义（χ2=4.706，P=0.030）。
治疗后，观察组的肺功能指标、炎症因子指标均优于对照组，差异有统计学意义（P<0.05）。
两组不良反应发生率比较，差异无统计学意义（P>0.05）。
结论对肺部IPFI患者采取伏立康唑治疗，可显著减轻患者不适症状和炎症状态，改善其肺功能，且不良反应发生率较低。
[关键词]伏立康唑；肺部侵袭性真菌感染；肺功能[中图分类号]R446.1 [文献标识码]A [文章编号]2096-1782（2023）03(b)-0060-04Effect of Voriconazole Treatment on Lung Function in Patients with Pul⁃monary Invasive Fungal InfectionXU Ya, SUN Yang, XU LinlinDepartment of Laboratory Medicine, Yancheng First People's Hospital, Yancheng, Jiangsu Province, 224000 China [Abstract] Objective Analyze the effect of voriconazole treatment on lung function in patients with invasive pulmo⁃nary fungal infection (IPFI). Methods60 patients with pulmonary IPFI admitted to the First People's Hospital of Yancheng City from July 2021 to December 2022 were selected as the study subjects. They were divided into two groups according to the envelope method. The control group (n=30) received routine treatment, while the observation group (n=30) received additional treatment with voriconazole. The treatment effects, lung function, inflammatory fac⁃tors, and adverse reactions of the two groups were compared. Results The effective rate of treatment in the observation group was 96.67%, which was higher than 73.33% in the control group, the difference was statistically significant (χ2= 4.706, P=0.030). After treatment, the lung function indicators and inflammatory factor indicators in the observation group were better than those in the control group, the difference was statistically significant (P<0.05). There was no statistically significant difference in the incidence of adverse reactions between the two groups (P>0.05). Conclusion The treatment of pulmonary IPFI patients with voriconazole can significantly reduce discomfort symptoms and inflam⁃matory status, improve lung function, and have a lower incidence of adverse reactions.[Key words] Voriconazole; Pulmonary invasive fungal infection; Lung function据调查[1]，肺部侵袭性真菌感染（invasive pulmo⁃nary fungal infection, IPFI）病死率高达35%~80%。
生物等效性研究指导原则 英文版
Technique Guideline for Human Bioavailability and BioequivalenceStudies on Chemical Drug ProductsContents(Ⅰ) Establishment and Validation for Biological Sample Analysis Methods (2)1. Common Analysis Methods (2)2. Method Validation (2)2.1 Specificity (2)2.2 Calibration Curve and Quantitative Scale (3)2.3 Lower Limit of Quantitation (LLOQ) (3)2.4 Precision and Accuracy (4)2.5 Sample Stability (4)2.6 Percent recovery of Extraction (4)2.7 Method Validation with microbiology and immunology (4)3. Methodology Quality Control (5)(Ⅱ) Design and Conduct of Studies (5)1. Cross-over Design (5)2. Selection of Subjects (6)2.1 Inclusion Criteria of Subjects: (6)2.2 Cases of Subjects (7)2.3 Division into Groups of the Subjects (7)3. Test and Reference Product, T and R (8)4. Sampling (8)(Ⅲ) Result Evaluation (9)(Ⅳ) Submission of the Contents of Clinical Study Reports (9)Technique Guideline for Human Bioavailability and BioequivalenceStudies on Chemical Drug ProductsSpecific Requirements for BA and BE Studies(Ⅰ) Establishment and Validation for Biological Sample Analysis MethodsBiological samples generally come from the whole blood, serum, plasma, urine or other tissues. These samples have the characteristics such as little quantity for sampling, low drug concentration, much interference from endogenous substances, and great discrepancies between individuals. Therefore, according to the structure, biological medium and prospective concentration scale of the analytes, it is necessary to establish the proper quantitative assay methods for biological samples and to validate such methods.1. Common Analysis MethodsCommonly used analysis methods at present are as follows: (1) Chromatography: Gas Chromatography(GS), High Performance Liquid Chromatography (HPLC), Chromatography－mass Spectrometry (LC-MS, LC-MS-MS, GC-MS, GC-MS-MS), and so on. All the methods above can be used in detecting most of drugs; (2) Immunology methods: radiate immune analysis, enzyme immune analysis, fluorescent immune analysis and so on, all these can detect protein and polypeptide; (3) Microbiological methods: used in detecting antibiotic drug.Feasible and sensitive methods should be selected for biologic sample analysis as far as possible.2. Method ValidationEstablishment of reliable and reproducible quantitative assay methods is one of the keys to bioequivalence study. In order to ensure the method reliable, it is necessary to validate the method entirely and the following aspects should be generally inspected:2.1 SpecificityIt is the ability that the analysis method has to detect the analytes exactly and exclusively, when interference ingredient exists in the sample. Evidences should be provided that the analytes are the primary forms or specific active metabolites of the test drugs. Endogenous instances, the relevant metabolites and degradation products in biologic samples should not interfere with the detection of samples. If there are several analytes, each should be ensured not to be interfered, and the optimal detecting conditions of the analysis method should be maintained. As for chromatography, at least 6 samples from different subjects, which include chromatogram of blank biological samples, chromatogram of blank biologic samples added control substance (concentration labeled) and chromatogram of biologic samples after the administration should beexamined to reflect the specificity of the analytical procedure. As for mass spectra (LC-MS andLC-MS-MS) based on soft ionization, the medium effect such as ion suppression should be considered during analytic process.2.2 Calibration Curve and Quantitative ScaleCalibration curve reflects the relationship between the analyte concentration and the equipment response value and it is usually evaluated by the regression equation obtained from regression analysis (such as the weighted least squares method). The linear equation and correlation coefficient of the calibration curve should be provided to illustrate the degree of their linear correlation. The concentration scale of calibration curves is the quantitative scale. The examined results of concentration in the quantitative scale should reach the required precision and accuracy in the experiment.Dispensing calibration samples should use the same biological medium as that for analyte, and the respective calibration curve should be prepared for different biological samples. The number of calibration concentration points for establishing calibration curve lies on the possible concentration scale of the analyte and on the properties of relationship of analyte/response value. At least 6 concentration points should be used to establish calibration curve, more concentration points are needed as for non-linear correlation. The quantitative scale should cover the whole concentration scale of biological samples and should not use extrapolation out of the quantitative scale to calculate concentrations of the analyte. Calibration curve establishment should be accompanied with blank biologic samples. But this point is only for evaluating interference and not used for calculating. When the warp* between the measured value and the labeled value of each concentration point on the calibration curve is within the acceptable scale, the curve is determined to be eligible. The acceptable scale is usually prescribed that the warp of minimum concentration point is within ±20% while others within ±15%. Only the eligible calibration curve can be carried out for the quantitative calculation of clinical samples. When linear scale is somewhat broad, the weighted method is recommended to calculate the calibration curve in order to obtain a more exact value for low concentration points. ( *: warp=[(measured value － labeled value)/labeled value]×100%)2.3 Lower Limit of Quantitation (LLOQ)Lower limit of quntitation is the lowest concentration point on the calibration curve, indicating the lowest drug concentration in the tested sample, which meets the requirements of accuracy and precision. LLOQ should be able to detect drug concentrations of samples in 3~5 eliminationhalf-life or detect the drug concentration which is 1/10~/20 of the C max. The accuracy of the detection should be within 80~120% of the real concentration and its RSD should be less than 20%. The conclusions should be validated by the results from at least 5 standard samples.2.4 Precision and AccuracyPrecision is, under the specific analysis conditions, the dispersive degree of a series of the detection data from the samples with the same concentration and in the same medium. Usually, the RSD from inter- or intra- batches of the quality control samples is applied to examine the precision of the method. Generally speaking, the RSD should be less than 15% and that around LLOQ should be less than 20%. Accuracy is the contiguous degree between the tested and the real concentrations of the biological samples (namely, the warp between the tested and the real concentrations of the quality-controlled samples). The accuracy can be obtained by repeatedly detecting the analysis samples of known concentration which should be within 85~115% and which around LLOQ should be within 80~120%.Generally, 3 quality-control samples with high, middle and low concentrations are selected for validating the precision and accuracy of the method. The low concentration is chosen within three times of LLOQ, the high one is close to the upper limit of the calibration curve, and the middle concentration is within the low and the high ones. When the precision of the intra-batches is detected, each concentration should be prepared and detected at least 5 samples. In order to obtain the precision of inter-batches, at least 3 qualified analytical batches, 45 samples should be consecutively prepared and detected in different days.2.5 Sample StabilityAccording to specific instances, as for biological samples containing drugs, their stabilities should be examined under different conditions such as the room temperature, freezing, thaw and at different preservation time, in order to ensure the suitable store conditions and preservation times. Another thing that should be paid attention to is that the stabilities of the stock solution and the analyte in the solution after being treated with, should also be examined to ensure the accuracy and reproducibility of the test results.2.6 Percent recovery of ExtractionThe recovery of extraction is the ratio between the responsive value of the analytes recovered from the biological samples and that of the standard, which has the same meaning as the ratio of the analytes extracted from the biologic samples to be analyzed. The recovery of extraction of the 3 concentrations at high, middle and low should be examined and their results should be precise and reproduceable.2.7 Method Validation with microbiology and immunologyThe analysis method validation above mainly aims at chromatography, with many parameters and principles also applicable for microbiological and immunological analysis. However, some special aspects should be considered in the method validation. The calibration curve of the microbiological and immunological analysis is non-linear essentially, so more concentration pointsshould be used to construct the calibration curve than the chemical analysis. The accuracy of the results is the key factor and if repetitive detection can improve the accuracy, the same procedures should be taken in the method validation and the unknown sample detection.3. Methodology Quality ControlThe unknown samples are detected only after the method validation for analysis of biological samples has been completed. The quality control should be carried out during the concentration detection of the biological samples in order to ensure the reliability of the method in the practical application. It is recommended to assess the method by preparing quality-control samples of different concentrations by isolated individuals.Each unknown sample is usually detected for only one time and redetected if necessary. In the bioequivalence experiments, biological samples from the same individual had better to be detected in the same batch. The new calibration curve should be established when detecting biological samples of each analysis batch and high, middle and low concentrations of the quality-control samples should be detected at the same time. Each concentration should at least have two samples and should be equally distributed in the detection sequence of the unknown samples. When there are a large number of unknown samples in one analysis batch, the number of the quality-control samples at different concentrations should be increased to make the quality-control samples exceed 5% of the unknown sample population. The warp of detection result from the quality-control samples should usually be less than 15%, while the warp of the low concentration point should be less than 20% and at most 1/3 results of the quality-control samples at different concentrations are allowed to exceed the limit. If the detection results of the quality-control samples do not accord with the above requirements, the detection results of the samples in this analysis batch should be blanked out.The samples with concentrations higher than the upper quantitation limit should be detected once more using corresponding diluted blank medium. As for those samples with concentrations lower than the lower quantitation limit, during pharmacokinetics analysis, those sampled before reaching C max should be calculated as zero while those after C max should be calculated as ND (Not detectable), so as to decrease the effect of the zero value on the AUG calculation.(Ⅱ) Design and Conduct of Studies1. Cross-over DesignCurrently, the crossover design is the most wildly applied method in the BE study. As for the drug absorption and clearance, there is a transparent variation among individuals. Therefore, the coefficient of variability among individuals is far greater than that of the individual himself. That is why the bioequivalence study is generally required to be designed on the principle of self crossover control. Subjects are randomly divided into several groups and treated in sequence, of whichsubjects in one group take the test products first and then the reference product, while subjects in the other take the reference products first and then the test products. A long enough interval is essential between the two sequences, which is called Wash-out period. In this way, every subject has been treated twice or more times sequentially, which is equal to self-control. Therefore, the influence of drug products on drug absorption can be discriminated from the others, and the effect of various test periods and individual difference on the results can be eliminated.Two-sequence crossover design, three-sequence crossover design are adopted respectively according to the amount of the test product. If two varieties of drug products are to be compared, the two-treatment, two-period or two-sequence crossover design will be a preferable choice. When three varieties of products (two test products and one reference product) are included, thethree-formulation, three-period and double 3×3 Latin square design will be the suitable choice. And a long enough wash-out period is required among respective periods.Wash-out period is set on purpose to eliminate the mutual disturbance of the two varieties of drug products and avoid the treatment in the prior period from affecting that of the next period. And the wash-out period is generally longer than or equal to 7 elimination half lives.While the half-lives of some drugs or their active metabolites are too long, it is not suitable to apply the crossover design. Under this circumstance, parallel design is adopted, but the sample size should be enlarged.However, as for some highly variable drugs, except for increase of the subjects, repetitive cross-over design can be applied, to test possibly existing difference in individual when receive the same preparation twice.2. Selection of Subjects2.1 Inclusion Criteria of Subjects:The difference among individuals of the subjects should be minimized so that the difference of the drug products can be detected. The inclusion criteria and exclusion criteria should be noted in the trial scheme.Male healthy subjects are recruited generally. And as to the drugs of special purpose, proper subjects are recruited according to specific conditions. If female healthy subjects are recruited, the possibility of gestation should be avoided. If the drugs to be tested have some known adverse effects, which may do harm to the subjects, patients can also be included as the subjects.Age: 18~ 40 years old generally. The difference in age of the subjects in one batch should not be more than 10 years.Body weight: not less than 50kg as to normal subjects. Body Mass Index (BMI), which is equal to body weight (kg)/ body height 2 (m2), is generally required to be in the range of standard body weight. For the subjects in one batch, the taken dosage is the same, the range of the bodyweight, therefore, should not have great disparity.The subjects should receive the overall physical examination and be proved healthy. There is not medical history of heart, kidney, digestive tract, nervous system, mental anomaly, metabolism dysfunction, and so on. The physical examination has revealed normal blood pressure, heart rate, electrocardiogram, and respiratory rate. Laboratory data have revealed normal hepatic function, renal function and blood function. Those examinations are essential to prevent the metabolism of drugs in vivo from being interfered by the diseases. According to the classification and safety of drugs, special items examinations are required before, during and after the test, such as the blood glucose examination, which is required in the drug trial of hypoglyceimic agents.In order to avoid the interference by other drugs, no administration of other drugs is allowed from two weeks before and till the end of the test. Moreover, the cigarette, wine,beverage with caffeine, or some fruit juice that may affect the metabolism of the drug, is forbidden during the trial period also. The subjects had better have no appetite of cigarette and wine. Possible effects of the cigarette-addicted history should not be neglected in the discussion of results.Due to the metabolism variance resulted by known genetic polymorphism of drugs, the safety factor which may be effected by the slow metabolism speed of drugs should be considered.2.2 Cases of SubjectsThe cases of the subjects should meet the statistic requirement. And according to the current statistical methods, 18~24 cases are enough for most drugs to meet the requirement of sample size. But as to some drugs of high variability, more cases may be required correspondingly.The cases of a clinic trial are determined by three fundamental factors: （1）Significance level: namely, the value of α, for which value 0.05 or 5% is often adopted;（2）Power of a test: namely, the value of 1-β. β is the index that represents the probability of the type error, which is also theⅡprobability of misjudging the actually efficacy drugs as inefficient drugs, and value not less than 80% is commonly stated; （3）Coefficient of variance（CV%）and Difference（θ）: In the equivalence test of two drugs, the greater CV% and θ of the test indexes are, the more cases are required. The CV% and θ are unknown before the trial and can only be estimated by the above parameters of the owned reference products or running the preliminary test. Moreover, when a BA test has been finished, the value of N can be calculated according to the parameters such as θ, CV% and 1-β and then compared with the cases adopted in the finished BA test to determine whether the cases are reasonable or not.2.3 Division into Groups of the SubjectsThe subjects should be randomly divided into different comparable groups. The cases of the two groups should guarantee the best comparability.3. Test and Reference Product, T and RThe quality of the reference products directly affect the results reliability of BE trial. Generally, the domestic innovator products of the same dosage form which has been approval to be on sale are commonly selected. If it failed in acquiring the innovator products, the key product on the market can also be chosen as the reference product and the related quality certifications (such as the test results of the assay and dissolution) and the reasons for option should be provided. When it comes to the drug study of specific purpose, other on-sale dosage forms which are of the same kind and similar with pharmaceutics properties are selected as the reference products and those reference products should be already on sale and qualified in quality. The difference in assay between the test product and reference product should not exceed 5%.The test product should be the scale-up product or manufacture scale product, which is consistent with the quality standards for clinical application. And the indexes such as the in vitro dissolution, stability, content or valence assay, consistency reports between batches should be provided to the test unit for reference. As for some drugs, the data of polymorphs and optical isomers should be offered additionally. The test and reference product should be noted with the advanced development unit, batch number, specification, storage conditions and expiry date.For future reference, the test and reference product should be kept long enough after the trialtill the product is approved to be on sale.4. SamplingThere is a significant sense in designing the sampling point to guarantee both the reliability of the trial results and the rationality of calculating the pharmacokinetics parameters. Commonly, there should be preliminary tests or the pharmacokinetics literatures at home and abroad served as the evidences of designing the reasonable sampling points. When the blood-drug concentration assay is performed, the absorption phase, balance phase and clearance phase should be considered overall. There must be enough sampling points in every phase of the C-T curve and around the T max. The concentration curve, therefore, can fully reflect the entire procedure of the drugs distribution in vivo. And the blank blood samples are taken before the administration. Then at least 2~3 points are sampled in the absorption phase, at least 3points are sampled near the C max and 3-5 points in the clearance phase. Try to avoid that the first point gets the C max, and running the preliminary test may avoid this. When the continuously-sampling results show that the drugs’ primary forms or the active metabolites are at the point of 3～5 half- lives or the blood drug concentration is 1/10～1/20 ofC max, the values of AUC0-t/AUC0-∞are generally bigger than 80% .For the terminal clearance item doesn’t affect the evaluation of the products’ absorption process much, as to the long half-life drugs, the sampling periods should be continued long enough, so that the whole absorption process can be compared and analyzed. In the multiple administration study, the BA of some drugs is known to beaffected by the circadian rhythm, samples of which should be taken 24 hours continuously if possible.When the BA of the test drugs can’t be determined by detecting the blood-drug concentration, if the primary forms and the active metabolites of the test drugs are mainly be excreted in urine (more than 70% of the dosage), the BA assay may be performed by detecting the urine drug concentration, which is the test of the accumulated excretion quantity of drugs in urine to reflect the intake of drugs. The test products and trial scheme should accord with the demands of BA assay. The urine samples should be collected at intervals, and the collection frequency and intervals of which should meet the demands of evaluating the excretion degree of the primary forms and the active metabolites of the test products in urine. However this method cannot reflect the absorption speed of the drugs and gets many error factors, it is not recommended generally.Some drugs metabolize so rapidly in vivo that it is impossible to detect the primary forms in biological samples. Under these circumstances, the method determining the concentration of corresponding active metabolites in biological samples is adopted to perform the BA and BE studies.(Ⅲ) Result EvaluationAt present, the weighting function of AUC on drug absorption degree is comparatively affirmed, while C max and T max sometimes are not sensitive and seemly enough for weighting the absorption speed due to their dependence on the arrangement of sampling time, and they are therefore not suitable for drug products with multi-peak phenomena and for experiments with large individual variation. During the evaluation, if there are some special instances of inequivalence, a specific analysis should be performed for specific problems.As for AUC，the 90% confidence interval is generally required within the scope of 80%~125%. As for the drugs with narrow treatment spectrum, the above scope should likely be appropriately reduced. While in a few instances, having been validated to be reasonable, the scope can also be increased. So does C max. And as for T max, statistical evaluation is required only when its release speed is closely correlated to clinical therapeutic effects and safety, the equivalence scope of which can be ascertained according to the clinical requirements.When bioavailability ratio of test products is higher than that of reference products, which is called suprabioavailability, the following two instances can be considered: 1). Whether the reference product itself is a product with low bioavailability, which results in the improvement of the test drug's bioavailability; 2). The quality of the reference product meets the requirement, and the test drug really has higher bioavailability.(Ⅳ) Submission of the Contents of Clinical Study ReportsIn order to satisfy the demand of evaluation, a clinical report of bioequivalence study shouldinclude the following contents: (1)Experiment subjective;(2) Establishment of analysis methods for bioavailability samples and data of inspection, as well as provision of the essential chromatograms;(3) Detailed experiment design and operation methods , including data of all the subjects，sample cases，reference products，given dosage，usage and arrangement of sampling time;(4) All data about original measurement of unknown sample concentrations，pharmacokinetics parameters and drug-time curve of each subjects;(5) Data handling procedure and statistical analysis methods as well as detailed procedure and results of statistics;(6) Observation results of clinical adverse reactions after taking medicine，midway exit and out of record of subjects and the reasons;(7) Result analysis and necessary discussion on bioavailability or bioequivalence; (8) References. A brief abstract is required before the main body; at the end of the main body, names of the experiment unit, chief persons of the study and experiment personnel should be signed to take the responsibility for the results of the study.。
结构方程模型构建流程
结构方程模型构建流程1.结构方程模型是一种统计分析方法，用于检验和建立变量之间的关系。
Structural equation modeling is a statistical analysis method used to test and establish relationships between variables.2.构建结构方程模型需要确定研究的理论模型和测量模型。
Building a structural equation model involvesestablishing the theoretical and measurement models of the research.3.在建立测量模型时，需要确定观察变量和潜在变量的关系。
In establishing the measurement model, it is necessary to determine the relationship between observed variables and latent variables.4.研究者需要利用统计软件来进行结构方程模型的估计和验证。
Researchers need to use statistical software to estimate and validate structural equation models.5.估计结构方程模型的参数需要通过最大似然估计或者贝叶斯估计等方法来完成。
Estimating the parameters of a structural equation model can be done through methods such as maximum likelihood estimation or Bayesian estimation.6.建立测量模型时，需要考虑到测量误差对变量之间关系的影响。
When establishing the measurement model, it is importantto consider the impact of measurement errors on the relationships between variables.7.潜在变量常常不容易直接观测到，需要通过观察变量来进行测量。
老年病人非心脏手术全麻苏醒延迟风险预测模型的建立与验证
[收稿日期]2022-06-01 [修回日期]2023-03-24[基金项目]安徽省高校自然科学研究项目(KJ2019A1258)[作者单位]皖西卫生职业学院附属医院1.麻醉科,2.骨科,安徽六安237000[作者简介]沈 俊(1984-),男,硕士,副主任医师.[通信作者]丁少成,主任医师.E⁃mail:w153********@[文章编号]1000⁃2200(2023)06⁃0766⁃06㊃临床医学㊃老年病人非心脏手术全麻苏醒延迟风险预测模型的建立与验证沈 俊1,李德奎1,李晓明1,杨 洋1,丁少成2[摘要]目的:建立老年病人非心脏手术全身麻醉苏醒延迟的风险预测模型,并评估其预测效能㊂方法:选择全身麻醉下行非心脏手术老年病人778例,年龄≥65岁,性别不限,ASA 分级Ⅱ~Ⅲ级㊂收集病人围手术期临床资料,记录苏醒延迟发生情况㊂对有意义的计量资料进行ROC 曲线分析,并计算其最佳截断值转变为分类资料㊂所有特征变量进行多因素logistic 回归分析调查苏醒延迟的独立危险因素,构建列线图预测模型㊂使用C 指数㊁校准图和决策曲线分析来评估预测模型的识别㊁校准和临床实用性㊂内部数据验证采用自举验证计算校正的C 指数㊂结果:最终共纳入718例老年病人,66例发生苏醒延迟,发生率为9.2%㊂预测列线图中包含的预测因素包括:年龄>74岁㊁术前MMSE 评分≤25分㊁术前Fried 表型评分≥3分㊁合并慢性阻塞性肺疾病㊁术中低血压㊁术中脑电双频指数<45(P <0.01)㊂该模型显示出良好的分辨力,C 指数为0.750(95%CI :0.679~0.821),且校准良好,在内部验证中校正C 指数达到0.743㊂决策曲线分析表明,Nomogram 模型预测术后发生苏醒延迟的风险阈值为2.0%~81.5%,此时增加临床收益㊂结论:基于年龄㊁术前MMSE 评分㊁术前Fried 衰弱表型评分㊁慢性阻塞性肺疾病史㊁术中低血压㊁术中脑电双频指数构建的苏醒延迟Nomogram 图预测模型可以方便地用于老年病人非心脏手术全身麻醉苏醒延迟的风险预测,预测效能良好㊂[关键词]全身麻醉;苏醒延迟;老年;风险预测模型[中图法分类号]R 614.2 [文献标志码]A DOI :10.13898/ki.issn.1000⁃2200.2023.06.013Establishment and validation of risk prediction model for delayed emergence from general anesthesia in elderly patients undergoing non⁃cardiac surgerySHEN Jun 1,LI De⁃kui 1,LI Xiao⁃ming 1,YANG Yang 1,DING Shao⁃cheng 2(1.Department of Anesthesiology ,2.Department of Orthopedics ,Affiliated Hospitalof West Anhui Health Vocational College ,Lu′an Anhui 237000,China )[Abstract ]Objective :To establish the risk prediction models for delayed emergence in elderly patients undergoing non⁃cardiacsurgery and evaluate the predictive efficiency.Methods :A total of 778elderly patients(both sexes,age≥65years)of ASA grade Ⅱ⁃Ⅲ,who undergoing non⁃cardiac surgery under general anesthesia were enrolled.The perioperative clinical data of patients were collected and the occurrence of delayed emergence was recorded.The ROC curve analysis was carried out for meaningful measurement date,andthe best cut⁃off value was calculated to be transformed into classified data.Multivariate logistic regression analysis was performed on all characteristic variables to investigate the independent risk factors of delayed emergence,and nomogram prediction model was drew.Discrimination,calibration and clinical usefulness of the predicting model were assessed using the C⁃index,calibration plot and decision curve analysis.Internal validation was assessed using the bootstrapping validation to calculate the corrected C⁃index.Results :A total of718elderly patients were enrolled in this study,66patients developed delayed emergence,and the incidence was 9.2%.Predictors contained in the prediction nomogram included age >74years,preoperative MMSE score≤25points,preoperative Fried′s phenotypescore≥3points,complication with chronic obstructive pulmonary disease,intraoperative hypotension,intraoperative bispectral index <45(P <0.01).The model displayed the good discrimination with a C⁃index of 0.750(95%CI :0.679-0.821)and good calibration.Corrected C⁃index value of 0.743was reached in the interval validation.Decision curve analysis showed that the nomogram model predicting the risk threshold of delayed emergence was 2.0%-81.5%,which increased the clinical benefit.Conclusions :This novel nomogram incorporating age,preoperative MMSE,preoperative Fried′s phenotype score,history of chronic obstructive pulmonary disease,intraoperative hypotension and intraoperative bispectral index can be conveniently used to facilitate the delayed emergence riskprediction in elderly patients undergoing non⁃cardiac surgery,which has the good predictive efficiency. [Key words ]general anesthesia;delayed emergence;elderly; risk prediction model 苏醒是全身麻醉(全麻)的一个重要阶段,其特征是病人从无意识状态恢复觉醒和意识㊂这个复杂的过程有着不同于诱导的精确的神经生物学机制[1]㊂全麻结束后90min内病人仍然不能自主睁眼或唤醒睁眼称之为苏醒延迟(delayed emergence, DE)[2-3],尽管全麻药物可以在几分钟内代谢清除,但部分病人仍然存在DE㊂目前,我国人口老龄化趋势越加明显,老年病人接受手术逐年增多,老年病人各系统功能呈退行性变化,对全麻药物敏感性高,内环境易紊乱,导致老年病人更易发生DE,也是麻醉医生面临的严峻挑战㊂研究[4]发现,未区分年龄时DE发生率为0.5%,而老年病人非心脏手术DE发生率目前尚无真实确切数据㊂随着精确麻醉理念和快速康复理念的不断深入,提高老年病人麻醉复苏质量对于促进老年病人围术期快速康复,减少相关并发症发生具有重要意义㊂DE的影响因素较多[5],本研究通过筛选老年病人非心脏手术全麻DE的危险因素,构建DE的预测体系并对其效能进行验证,为临床预防DE提供参考,现作报道㊂1 资料与方法1.1 一般资料 本研究查阅相关研究文献,并实施预实验,结果表明100例老年病人非心脏手术发生DE9例,发生率为9%,初步纳入6项可能与DE发生相关的危险因素,每项危险因素纳入10项结局事件,计算出样本量需要667(6×10/0.09)例,假设脱落率为15%,故需要样本778例㊂本研究方案经皖西卫生职业学院附属医院伦理委员会批准,选择2021年1月至2022年3月计划接受非心脏手术的老年病人778例作为研究对象,均与病人或病人家属签署研究知情同意书㊂纳入标准:年龄≥65岁,性别不限;美国麻醉师协会分级(ASA)Ⅱ~Ⅲ级;气管插管全麻;能够配合研究㊂1.2 方法 本研究由受过专门训练的研究人员进行术前访视与数据的收集,入组病人的特征信息通过皖西卫生职业学院附属医院麦迪斯顿手术麻醉信息系统收集,包括(1)一般资料:年龄㊁性别㊁ASA分级㊁体质量指数(BMI)㊁术前MMSE量表评分㊁术前Fried表型(Fried′s phenotype,FP)衰弱评分≥3分例数(FP量表由体质量变化㊁握力㊁疲乏㊁步速㊁身体活动量5个项目组成,评分标准:0分为无衰弱,1~2分为衰弱前期,≥3分为衰弱)[6]㊁吸烟史㊁饮酒史㊁基础疾病史[高血压㊁糖尿病㊁冠心病㊁慢性阻塞性肺部疾病(COPD)]㊁术前血红蛋白㊁术前总蛋白㊁白细胞㊁总胆红素㊁谷氨酸氨基转移酶㊁肌酐㊁血糖;(2)术中资料:手术类型㊁麻醉时间㊁全凭静脉麻醉㊁舒芬太尼用量㊁输液总量㊁出血量㊁术中血压降低(术中低血压定义:收缩压低于术前基准状态20%)㊁术中血压升高(术中高血压定义:收缩压高于术前基准状态20%)㊁术中低脑电双频指数(BIS) (BIS值<45)㊁术中平均体温㊂剔除标准:(1)心肺功能严重障碍㊁脑缺血性疾病;(2)视听力障碍;(3)合并精神分裂症,抑郁症;(4)行心血管手术以及神经外科手术;(5)术后转入ICU;(6)病人麻醉单缺失本研究所需的数据㊂1.3 DE的评估 病人麻醉停止即视为复苏开始,期间麻醉医生每隔10min采用Steward评分对病人进行评估,评分结果记录于手术麻醉系统中㊂其中Steward评分总分0~6分,<4分为未苏醒,≥4分为苏醒㊂在麻醉停止后90min时Steward评分<4分,定义为DE㊂1.4 统计学方法 采用t检验㊁χ2检验或Fisher精确概率法㊁ROC曲线㊁多因素logistic回归分析㊁Nomogram模型和决策曲线分析(decision curve analysis,DCA)㊂2 结果2.1 老年病人非心脏手术全麻DE发生的单因素分析 本研究共对778例全身麻醉非心脏手术老年病人进行回顾性分析,其中自愿退出本研究18例,术后转入ICU12例,资料不全30例,最终纳入718例,全身麻醉后共发生66例DE,发生率为9.2%㊂DE的发生与年龄增加㊁术前MMSE评分减低㊁术前FP评分增加㊁合并COPD㊁术前血红蛋白浓度减低㊁麻醉时间延长㊁术中发生低血压㊁术中BIS值<45有关(P<0.05~P<0.01)(见表1)㊂表1 老年病人非心脏手术全麻DE发生的单因素分析(x±s)变量非DE(n=652)DE(n=66)t P 年龄/岁70±577±610.63<0.01性别 男 女36328936300.03*>0.05 ASA分级 Ⅱ Ⅲ40424840260.05*>0.05 BMI/(kg/m2)24.5±3.224.7±3.60.37>0.05术前MMSE评分/分26.2±1.824.7±1.4 6.57<0.01 FP评分≥3分602026.95*<0.01吸烟史140160.27*>0.05饮酒史222240.14*>0.05高血压99120.41*>0.05续表1变量非DE(n=652)DE(n=66)t P糖尿病456 >0.05△冠心病294 >0.05△COPD259 <0.01△术前血红蛋白/(g/L)107.2±16.198.9±13.6 4.04<0.01术前总蛋白/(g/L)59.8±6.558.6±6.8 1.11>0.05术前白细胞/(109/L) 6.4±1.5 6.7±1.6 1.20>0.05术前总胆红素/(μmol/L)13.6±3.813.8±3.50.32>0.05术前谷氨酸氨基转移酶/(U/L)23.2±6.824.1±7.30.79>0.05术前肌酐/(μmol/L)62.8±8.665.4±8.3 1.82>0.05术前血糖/(mmol/L) 5.9±1.7 6.1±1.90.71>0.05术前血钠/(mmol/L)139.8±3.3139.5±3.50.55>0.05术前血钾/(mmol/L) 4.3±0.5 4.4±0.6 1.19>0.05手术类型 腹腔镜手术298280.26*>0.05 骨科手术193210.14*>0.05 泌尿外科手术7070.00*>0.05 胸腔镜手术2020.00*>0.05麻醉时间/min140.3±27.1147.9±26.8 2.91<0.05全凭静脉麻醉15260.25*>0.05舒芬太尼用量/μg47.2±8.3 46.1±8.60.80>0.05输液量/mL1215.6±178.4 1238.7±180.90.78>0.05术中失血量/mL53.6±11.5 52.7±11.80.47>0.05术中低血压521618.50*<0.01术中高血压5980.67*>0.05术中BIS<45731711.81*<0.01术中平均体温/℃36.4±0.436.3±0.4 1.51>0.05 *示χ2值;△示Fisher精确概率法2.2 DE相关因素的ROC分析 将表1结果中差异有统计学意义的计量资料进行ROC曲线分析,计算最佳截断值㊂结果显示:年龄㊁术前MMSE评分㊁术前血红蛋白水平㊁麻醉时间的AUC分别为0.823㊁0.725㊁0.673㊁0.609;最佳截断值分别为:>74岁㊁≤25分㊁≤103.7g/L㊁>142min(见图1㊁表2)㊂2.3 DE发生风险因素的多因素logistic回归分析 将表1中有统计学意义的计数资料指标以及表2中的变量转化为二分类计数资料进行多因素logistic回归分析,采用逐步回归法,Hosmer⁃Lemeshow模型拟合度有统计学意义(χ2=1.69,P> 0.05),结果表明:年龄(>74岁)㊁术前MMSE评分(≤25分)㊁术前FP评分(≥3分)㊁合并COPD (是)㊁术中低血压(是)㊁术中BIS<45(是)是老年病人非心脏手术全麻DE发生的危险因素(P<0.01)(见表3)㊂ 表2 老年病人非心脏手术全麻DE相关变量ROC曲线分析变量AUCYouden指数最佳截断值 P95%CI敏感度/% 特异度/%年龄0.8720.507>74岁 <0.01 0.793~0.85066.784.1术前MMSE评分0.7130.356≤25分<0.010.690~0.75772.762.9术前血红蛋白0.6730.341≤103.7g/L<0.010.638~0.70874.259.8麻醉时间0.6690.227>142min<0.010.572~0.64566.756.0 表3 老年病人非心脏手术发生DE的多因素logistic回归分析因素B SE Waldχ2OR(95%CI)P 年龄(>74岁vs.≤74岁) 1.5330.37516.71 4.633(2.221~9.661)<0.01 MMSE(≤25分vs.>25分) 1.5540.43112.974.730(2.030~11.017)<0.01 FP(≥3分vs.<3分) 1.5930.34021.984.918(2.527~9.571)<0.01合并COPD(是vs.否) 1.3990.693 4.08 4.050(1.042~15.745)<0.01术前血红蛋白(≤103.7g/L vs.>103.7g/L)0.6690.4132.631.953(0.870~4.385)>0.05麻醉时间(>142min vs.≤142min)0.2720.4450.381.313(0.549~3.138)>0.05术中低血压(是vs.否) 1.6930.36821.195.435(2.644~11.174)<0.01术中BIS<45(是vs.否) 1.5710.36418.594.812(2.356~9.829)<0.01 2.4 DE的Nomogram模型构建 将年龄>74岁㊁术前MMSE评分≤25分㊁术前FP评分≥3分㊁合并COPD㊁术中低血压㊁术中BIS<45作为构建预测老年病人非心脏手术发生DE的预测因子,绘制Nomogram模型(见图2)㊂R语言结果显示C⁃index 为0.750(95%CI:0.679~0.821),内部验证显示校正C指数为0.743,说明此Nomogram模型具有中等的预测能力㊂2.5 Nomogram模型的校正与决策曲线评价 首先绘制预测老年病人非心脏手术发生DE的Nomogram模型的校准曲线(见图3),对角线虚线代表理想模型的预测,虚点线表示本研究构建的Nomogram性能,实线表示对构建的Nomogram的校正曲线,其越接近对角线虚线表示预测性能越佳㊂使用 rmda”包进行DCA 分析评估本研究构建的Nomogram 模型临床收益,结果表明Nomogram 模型预测DE 风险阈值为2.0%~81.5%,此时能为临床增加预测收益(见图4)㊂3 讨论 本队列研究结果显示:老年病人非心脏手术全身麻醉DE 发生率为9.2%,既往对老年病人非心脏手术DE 的临床研究数据很少,大多数可获得的公开材料由病例报告组成[7]㊂ZELCER 等[8]报道443例混合类型手术病人在全麻后15~90min 内无反应的发生率为9.46%,本研究结果与其相近㊂DE 临床上表现为镇静㊁缺乏主动性和对刺激缺乏足够的反应㊂其影响因素众多,大部分是可逆转的因素,很小部分可能是脑缺血缺氧性损伤或卒中等器质性疾病导致[9]㊂本研究针对影响老年病人DE 的因素进行筛查,对于有意义的计量资料进行ROC 分析,计算其AUC 和最佳截断值,并根据最佳截断值将有意义的计量资料转变为二分类计数资料,多因素logistic 回归分析结果表明,年龄>74岁㊁术前MMSE 评分≤25分㊁术前FP 评分≥3分㊁合并COPD㊁术中低血压㊁术中BIS <45是老年病人非心脏手术发生DE 的危险因素㊂为了直观地将影响DE 的因素展现出来,我们构建预测老年病人非心脏手术全麻DE 的Nomogram 模型㊂列线图在医学中被广泛用作预测预后的工具,具有用户友好的数字界面,更容易理解,帮助临床做出决策㊂本研究构建的预测DE Nomogram 模型的C 指数为0.750,队列的内部验证得出校正C 指数为0.743,显示出该模型具有良好的辨别力㊂校正曲线与决策曲线分析均证实该DE Nomogram 模型具有较好的准确性,预测DE 风险阈值为2.0%~81.5%,能够为临床预测DE 的发生提供决策支持㊂研究[10]发现随着年龄的增加大脑微血管再生机制受损,微血管血供能力降低,导致神经元供血减少,血脑屏障的完整性及功能受损可导致神经元过度氧化应激反应,导致神经退行性变㊂神经系统退行性变导致病人对全身麻醉剂㊁阿片类药物和苯二氮卓类的敏感性增加[11]㊂老年人麻醉后的脑电波与年轻人明显不同,主要以δ波为主,同时脑电爆发性抑制的发生明显增加,这同样增加了对麻醉药的敏感性[12]㊂老年人体内的分布体积㊁清除率和血浆蛋白结合的减少导致药物的游离血浆浓度增高㊂以上的因素共同导致了老龄化病人麻醉药物产生的效应更加持久而代谢相对减缓,从而导致DE 的发生增加㊂MMSE 评分是评测认知功能常用量表,既往研究表明术前认知功能降低是老年病人非心脏手术术后谵妄发生的独立危险因素[13],使用MMSE 评分来预测老年病人非心脏手术DE 发生的风险同样也是可行的[14],值得注意的是相当部分的谵妄发生在复苏室内,它可能是DE 的一种原因,主要表现为睡眠增多,表情淡漠㊁语速及动作缓慢等活动受抑表现[15],此外认知功能减退也是中枢神经系统退行性变的一种表现㊂衰弱是老龄化人口最重要的特征之一,衰弱病人的生活自理能力明显降低,同时增加术后不良事件发生率[16]㊂研究[17]发现,75岁及以上社区老年人中55.7%存在身体衰弱,40.3%合并有轻度认知障碍,故麻醉医生对 衰弱及其对围手术期的影响”的关注度越来越高,但目前术前衰弱与DE的研究较少㊂还有研究[18]发现术前身体衰弱是老年病人行非心脏手术发生术后谵妄的危险因素;黄煦晨[19]发现合并衰弱的老年病人行无痛胃肠镜检查时的苏醒时间明显延长㊂衰弱导致DE发生率增加可能与以下因素有关:(1)肌肉组织萎缩,脂肪相对增多,会使亲脂性的麻醉药物作用时间更长;(2)肾脏萎缩,麻醉药物在肾脏的排泄速度降低;(3)肺实质弹性逐渐减低,功能性肺泡表面积减少,呼吸肌功能降低,导致通气/血流灌注比例失调,拔管后低氧血症和肺不张发生风险增加[20]㊂笔者强调,迫切需要将术前虚弱评估作为接受手术的老年人的风险分层工具,以弥补常用的风险预测工具,如年龄㊁美国麻醉医师协会(ASA)分级㊁代谢当量评分(METS)等无法衡量到的麻醉风险,做到针对性地预防,改善老年病人的预后与康复㊂COPD的最主要特征是气流的不可逆受阻㊁肺泡通气量不足以及通气/血流比例失调[21],合并COPD导致DE发生率增加可能与以下因素有关:(1)肺泡通气量不足导致残留的吸入麻醉药排泄减缓;(2)麻醉机械通气后通气/血流比例进一步失衡,肺通气和肺换气受限,进一步损害COPD病人的气体交换,导致高碳酸血症进而影响麻醉复苏[22]㊂术中低血压在老年病人非心脏手术期间较为常见,研究[23]表明,术中低血压可能导致术后30d内重要器官缺血和术后死亡率增加㊂术中低血压可导致脑缺血缺氧,即使一过性的缺氧也可能造成脑组织不可逆性的水肿和坏死,这种改变在老年人中更加明显㊂此外老年人的脑血管自动调节能力受损,对低血压的代偿作用减弱,导致脑低灌注,使大脑功能恶化,这种影响主要表现为术后认知功能改变以及脑卒中的发生[24],这些改变也会导致病人的苏醒时间延长,是否能够通过预防术中低血压的发生减少老年病人术后DE的发生值得进一步研究㊂BIS能够较好地反映大脑皮层的功能状态,减少全麻药用量,研究[25]发现使用BIS进行监测,能够缩短病人拔管时间㊁睁眼时间以及定向力恢复时间㊂亦有研究[26]发现,老年病人胃肠道手术术中维持较低的BIS(40~49)导致术后意识恢复时间明显延长㊂临床上将BIS值<45作为深麻醉状态的标准[27],术中BIS值<45导致DE发生可能与深麻醉状态下脑电爆发性抑制增加,脑灌注减少对麻醉药物敏感性增加有关[12,28]㊂针对本研究发现的影响老年病人非心脏术的危险因素,我们应该做到有的放矢,有针对性地干预,积极制定术前㊁术中㊁术后各项有力措施㊂DE的发生是多种因素导致的,也可能是其他术后不良转归的结果,当出现DE时应该维持病人血流动力学㊁内环境稳定,积极纠正可逆因素,减少并发症发生㊂但本研究具有一定的局限性:(1)既往鲜见针对老年病人的DE研究,DE的发生率无法参考;(2)预测模型为单中心研究,DE的发生率以及预测模型的准确性有待多中心研究验证;(3)影响DE的因素可能纳入不全,可能遗漏一部分有意义因素(如苏醒时的体温保护㊁术后疼痛控制㊁神经阻滞的应用㊁术中输血等)㊂[参考文献][1] 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婴幼儿呼吸道合胞病毒中-_重度感染的预测模型构建与验证
574研究论著新医学 2023年8月第54卷第8期婴幼儿呼吸道合胞病毒中-重度感染的预测模型构建与验证吴传飞 余佩 宣传富【摘要】目的探讨婴幼儿呼吸道合胞病毒（RSV）中-重度感染的危险因素，建立预测模型并验证。
方法回顾性分析399例RSV感染患儿的临床资料，其中299例为建模组、100例为验证组。
采用单因素和多因素Logistic回归分析筛选出中-重度感染的危险因素，并建立临床评分模型。
结果建模组299例RSV感染患儿中，判定为中-重度48例、轻度251例。
根据单因素及多因素Logistic 回归分析筛选出体重、喂养史、是否喘息、红细胞分布宽度、红细胞压积等影响因素（P均< 0.05），用于拟合联合诊断，制作临床评分模型。
该临床评分模型的曲线下面积为0.777（95%CI 0.703～0.853），诊断阈值为1.365，此时灵敏度为0.829、特异度为0.604，内部验证结果表明该模型有较好的一致性。
结论建立了预测RSV中-重度感染的临床评分模型，该评分模型具有一定准确度。
【关键词】婴幼儿；呼吸道合胞病毒；感染；中-重度；预测模型Establishment and validation of a predictive model for moderate and severe respiratory syncytial virus infection in infants Wu Chuanfei， Yu Pei， Xuan Chuanfu. Department of Pediatrics， Yiwu Maternity and Children Hospital， Yiwu 322000， China Corresponding author， Wu Chuanfei， E-mail:****************【Abstract】Objective To explore the risk factors for moderate and severe respiratory syncytial virus （RSV） infection in infants， and to establish and validate the predictive model. Methods Clinical data of 399 children with RSV infection were retrospectively analyzed， including 299 cases in the model group and 100 cases in the validation group. Univariate and multivariate Logistic regression analyses were used to screen the risk factors of moderate and severe RSV infection， and a clinical scoring model was established. Results In the model group （n = 299）， 48 children were classi fi ed with moderate to severe RSV infection and 251 cases of mild RSV infection. According to univariate and multivariate Logistic regression analyses， body weight， feeding history， wheezing，erythrocyte distribution width and hematocrit were the risk factors （all P < 0.05）， which were used to fit the joint diagnosis and establish the clinical scoring model. The area under the ROC curve （AUC） of clinical scoring model was 0.777 （95%CI 0.703-0.853），the diagnostic cuto ff value was 1.365， the sensitivity was 0.829 and the speci fi city was 0.604， respectively. The internal validation results showed that the model had high consistency. Conclusion A clinical scoring model for predicting moderate and severe RSV infection is established， which has certain accuracy.【Key words】 Infant； Respiratory syncytial virus； Infection； Moderate to severe； Prediction model呼吸道合胞病毒（RSV）是一种严重威胁婴幼儿生命健康的病原体，几乎所有儿童在2岁之前都曾有过RSV感染［1］。
胸腰椎骨折内固定术后合并下肢DVT风险预测列线图模型的建立与验证
胸腰椎骨折内固定术后合并下肢D V T风险预测列线图模型的建立与验证沈晓琴,朱月华,张 燕,金 荣,袁 园,彭 霞,常雪娇E s t a b l i s h m e n t a n d v a l i d a t i o n o f aN o m o g r a m m o d e l f o r r i s k p r e d i c t i o n o f l o w e r e x t r e m i t y DV Ta f t e r i n t e r n a l f i x a t i o no f t h o r a c o l u m b a r f r a c t u r e sS H E NX i a o q i n ,Z H UY u e h u a ,Z H A N GY a n ,J I NR o n g ,Y U A NY u a n ,P E N GX i a ,C H A N GX u e ji a o (D o n g t a i P e o p l e 's H o s p i t a l ,J i a n gs u224200C h i n a )A b s t r a c t O b je c t i v e :T o e x p l o r et h er i s kf a c t o r s o fl o w e re x t r e m i t y d e e p v e i n t h r o m b o s i s (D V T )a f t e ri n t e r n a lf i x a t i o n o f t h o r a c o l u m b a r f r a c t u r e s ,a n de s t a b l i s har i s k p r e d i c t i o n N o m o gr a m m o d e l .M e t h o d s :T h ec l i n i c a ld a t ao f225p a t i e n t sw h or e c e i v e d i n t e r n a l f i x a t i o ni nh o s p i t a ld u et ot h o r a c o l u m b a rf r a c t u r e sf r o m M a y 2019t o M a y 2022w e r er e t r o s p e c t i v e l y a n a l yz e d ,a n dt h e p a t i e n t sw e r e d i v i d e d i n t o t h e c o m b i n e d g r o u p (35c a s e s )a n dt h en o n -c o m b i n e d g r o u p (190c a s e s )a c c o r d i n g t ow h e t h e r t h e y w e r e c o m b i n e dw i t h l o w e r e x t r e m i t y D V Ta f t e r s u r g e r y .M u l t i v a r i a t eL o g i s t i c r e g r e s s i o na n a l y s i sw a su s e dt oa n a l yz e t h er i s kf a c t o r so f l o w e r e x t r e m i t y D V Ta f t e r i n t e r n a l f i x a t i o no f t h o r a c o l u m b a r f r a c t u r e s ,a n dt h er i s k p r e d i c t i o n N o m o g r a m m o d e lw a se s t a b l i s h e d .R e c e i v e ro p e r a t i n g c h a r a c t e r i s t i cc u r v e (R O C )w a s p l o t t e dt oe v a l u a t et h e p r e d i c t i o n e f f i c i e n c y o ft h e N o m o gr a m m o d e l ,a n d c a l i b r a t i o n c u r v ew a s p l o t t e d t o i n t e r n a l l y v e r i f y t h e d i f f e r e n t i a t i o no f t h e p r e d i c t i v eN o m o g r a m m o d e l u s i n g B o o t s t r a p s e l f -s a m p l i n gm e t h o d .R e s u l t s :T h e p r o p o r t i o n so f p a t i e n t sa g e dȡ60y e a r so l d ,A S I A g r a d e A ,i n t r a o pe r a t i v eb l o o dl o s sȡ1000m L ,b l o o d t r a n sf u s i o n ,p o s t o p e r a t i v eb e d t i m e ȡ4da n d p o s t o p e r a t i v eD -d i m e r l e v e l i nt h e c o m b i n e dg r o u p w e r ehi gh e r t h a nt h o s e i nt h en o n -c o m b i n e d g r o u p (P <0.05).M u l t i v a r i a t eL o g i s t i cr e g r e s s i o na n a l y s i ss h o w e dt h a t a g e ȡ60y e a r so l d ,A S I A g r a d eA ,i n t r a o pe r a t i v e b l o o d l o s s ȡ1000m L ,b l o o dt r a n sf u s i o n ,h igh l e v e l o f p o s t o p e r a ti v eD -d i m e r ,p o s t o pe r a t i v eb e dt i m e ȡ4dw e r ea l l r i s kf a c t o r s f o r l o w e r e x t r e m i t y DV Ta f t e r i n t e r n a l f i x a t i o no f t h o r a c o l u m b a r f r a c t u r e s (P <0.05).T h ea b o v er i s kf a c t o r sw e r e t a k e na s p r e d i c t i v e i n d i c a t o r s ,t h er i s k p r e d i c t i o n N o m o g r a m m o d e lo fl o w e re x t r e m i t y DV T a f t e ri n t e r n a lf i x a t i o no ft h o r a c o l u m b a rf r a c t u r e s w a s c o n s t r u c t e d .R O Cc u r v e a n a l y s i sr e s u l t ss h o w e dt h a t t h ea r e au n d e r t h ec u r v e (A U C )o f l o w e re x t r e m i t y D V Ta f t e r i n t e r n a l f i x a t i o no f t h o r a c o l u m b a r f r a c t u r e sw a s 0.797[95%C I (0.739,0.848)],t h es e n s i t i v i t y w a s74.29%,a n dt h es p e c i f i c i t y wa s86.84%.T h e i n t e r n a l v e r i f i c a t i o nc o n s i s t e n c y i n d e x o f B o o t s t r a p s e l f -s a m p l i n g m e t h o dw a s 0.793,i n d i c a t i n g go o d d i f f e r e n t i a t i o n .T h e c a l i b r a t i o n c u r v e f i t t e d t h e s t a n d a r d c u r v ew e l l .C o n c l u s i o n s :A g e ȡ60y e a r s o l d ,A S I A g r a d eA ,i n t r a o p e r a t i v eb l o o d l o s s ȡ1000m L ,b l o o d t r a n s f u s i o n ,h i gh l e v e l o f p o s t o p e r a t i v eD -d i m e r ,a n d p o s t o p e r a t i v e b e d t i m e ȡ4d a r e a l l r i s k f a c t o r s f o r l o w e r e x t r e m i t y D V Ta f t e r i n t e r n a l f i x a t i o no f t h o r a c o l u m b a r f r a c t u r e s .T h e N o m o g r a m m o d e l c o n s t r u c t e db a s e do nt h i s i sh e l p f u l f o rc l i n i c a l s c r e e n i n g o fh i g h -r i s k p a t i e n t st o p r e v e n t t h e o c c u r r e n c e o f l o w e r e x t r e m i t y D V Ta f t e r s u r g e r y.K e yw o r d s t h o r a c o l u m b a r f r a c t u r e s ;l o w e r e x t r e m i t y d e e p v e i n t h r o m b o s i s ;i n t e r n a l f i x a t i o n ;r i s k f a c t o r s ;n o m o g r a m 摘要 目的:探讨胸腰椎骨折内固定术后合并下肢深静脉血栓形成(D V T )的危险因素,并据此建立风险预测列线图模型㊂方法:回顾性分析2019年5月 2022年5月因胸腰椎骨折于医院接受内固定术的225例病人的临床资料,并根据术后是否合并下肢D V T 将病人分为合并组(35例)和未合并组(190例)㊂采用多因素L o g i s t i c 回归分析法分析胸腰椎骨折内固定术后合并下肢D V T 的危险因素,并建立风险预测列线图模型㊂绘制受试者工作特征曲线(R O C )以评估列线图模型的预测效能,并绘制校准曲线,采用B o o t s t r a p 自抽样法内部验证预测列线图模型的区分度㊂结果:合并组年龄ȡ60岁㊁美国脊柱损伤协会(A S I A )分级A 级㊁术中失血量ȡ1000m L ㊁输血㊁术后卧床时间ȡ4d 的病人占比及术后D -二聚体水平均高于未合并组(P <0.05);多因素L o gi s t i c 回归分析结果显示,年龄ȡ60岁㊁A S I A 分级A 级㊁术中失血量ȡ1000m L ㊁输血㊁术后D -二聚体水平高㊁术后卧床时间ȡ4d 均是胸腰椎骨折内固定术后合并下肢D V T 的危险因素(P <0.05);将上述危险因素作为预测指标,构建胸腰椎骨折内固定术后合并下肢D V T 的风险预测列线图模型;R O C 曲线分析结果显示,列线图预测胸腰椎骨折内固定术后合并下肢D V T 的曲线下面积(A U C )为0.797[95%C I(0.739,0.848)],灵敏度为74.29%,特异度为86.84%;采用B o o t s t r a p 自抽样法内部验证一致性指数为0.793,提示区分度良好;校准曲线与标准曲线贴合较好㊂结论:年龄ȡ60岁㊁A S I A 分级A 级㊁术中失血量ȡ1000m L ㊁输血㊁术后D -二聚体水平高㊁术后卧床时间ȡ4d 均是胸腰椎骨折内固定术后合并下肢D V T 的危险因素,据此构建的列线图模型有助于临床筛选高风险病人以预防病人术后下肢D V T 发生㊂关键词 胸腰椎骨折;下肢深静脉血栓;内固定术;危险因素;列线图d o i :10.12104/j.i s s n .1674-4748.2023.28.003作者简介 沈晓琴,副主任护师,本科,单位:224200,东台市人民医院;朱月华(通讯作者)㊁张燕㊁金荣㊁袁园㊁彭霞㊁常雪娇单位:224200,东台市人民医院㊂引用信息 沈晓琴,朱月华,张燕,等.胸腰椎骨折内固定术后合并下肢D V T 风险预测列线图模型的建立与验证[J ].全科护理,2023,21(28):3901-3905.㊃1093㊃全科护理2023年10月第21卷第28期Copyright ©博看网. All Rights Reserved.胸腰椎骨折多由车祸事故或高空坠落引起,临床常采用内固定术治疗[1]㊂病人术后需卧床制动,下肢活动受限,静脉血流减缓,加之手术引起的血小板功能亢进㊁血管内皮受损㊁凝血因子激活等导致血液高凝,增加下肢深静脉血栓形成(D V T)发生风险[2]㊂研究发现,胸腰椎骨折术后下肢D V T的发生率为3.45%~ 21.43%[3]㊂该类病人早期隐匿性较强,极易被病人和医护人员忽视,发现时往往已经出现胸痛㊁胸闷等肺栓塞症状,严重的甚至危及病人生命健康[4]㊂因此,明确胸腰椎骨折内固定术后合并下肢D V T的危险因素,并建立有效的风险预测模型,对预防下肢D V T㊁提高临床治疗效果具有重要意义㊂列线图常用于表达风险预测模型中各变量之间的关系,使结果更加清晰直观,用于预测不良事件发生的风险[5]㊂研究报道,老年髋部骨折病人术后D V T风险列线图预测模型的建立可为临床预测此类疾病提供科学指导[6]㊂另有研究发现,构建外科手术后下肢D V T的风险预测模型可为预防外科手术病人术后下肢D V T发生提供参考依据[7]㊂但目前尚缺乏预测胸腰椎骨折内固定术后合并下肢D V T发生风险的列线图模型,故本研究将探讨胸腰椎骨折内固定术后合并下肢D V T的危险因素,并构建风险列线图预测模型,旨在为临床防治提供参考,详情如下㊂1资料与方法1.1一般资料回顾性分析本院2019年5月 2022年5月收治的行内固定术的225例胸腰椎骨折病人,其中男93例,女132例;年龄39~76(58.76ʃ10.59)岁;胸椎骨折56例,腰椎骨折169例㊂纳入标准:经影像学检查确诊为胸腰椎骨折;行内固定术治疗;术前检查凝血功能正常;临床资料和随访资料完整㊂排除标准:既往有下肢D V T史;下肢静脉瓣膜功能不全;合并其他部位骨折;近期服用过抗凝药物;短期内进行过重大手术;严重肝肾功能障碍;伴有恶性肿瘤;伴有严重感染性疾病;伴有血液系统疾病;伴有认知功能障碍或其他精神疾病㊂1.2调查方法收集病人临床资料,包括性别㊁年龄㊁体质指数㊁合并症(高血压㊁糖尿病㊁冠心病)㊁美国脊柱损伤协会(A S I A)分级㊁损伤节段㊁术中失血量㊁有无输血㊁手术时间㊁术后实验室检测指标(D-二聚体㊁血小板计数)㊁术后卧床时间㊁是否使用抗凝药物等㊂下肢D V T诊断标准[8]:下肢肿胀㊁疼痛,小腿后方或大腿内侧有压痛;血液处于高凝状态;D-二聚体> 0.5m g/L;超声检查显示静脉血管不能压闭,管腔内有不规则回声;静脉血栓部位无或仅有少量血流信号;静脉血管不能充分充盈或充盈后有缺损㊂根据病人术后是否合并D V T将其分为合并组(35例)和未合并组(190例)㊂1.3观察指标1)合并组与未合并组临床资料比较㊂2)胸腰椎骨折内固定术后合并下肢D V T影响因素的多因素L o g i s t i c回归分析,观察其比值比(O R)和95%置信区间(C I)㊂3)胸腰椎骨折内固定术后合并下肢D V T预测列线图模型的构建与验证㊂1.4统计学方法采用S P S S26.0软件对数据进行统计分析㊂定量资料均符合正态分布,采用均数ʃ标准差(xʃs)描述,组间比较采用独立样本t检验;定性资料采用例数㊁百分比(%)描述,组间比较采用χ2检验,多组定性资料比较中两两比较采用分割法,并校正检验标准;采用多因素L o g i s t i c回归分析法对胸腰椎骨折内固定术后合并下肢D V T的危险因素进行分析;采用R(R3.6.1)软件包,应用r m s程序包构建列线图模型,以受试者工作特征曲线(R O C)评估列线图模型的预测效能,绘制校准曲线;采用B o o t s t r a p自抽样法内部验证预测列线图模型的区分度,一致性指数越接近1表示区分度越好㊂以P<0.05为差异有统计学意义㊂2结果2.1合并组与未合并组临床资料比较合并组与未合并组性别㊁体质指数㊁合并高血压㊁合并糖尿病㊁合并冠心病㊁损伤节段㊁手术时间㊁血小板计数和使用抗凝药物情况比较差异均无统计学意义(P>0.05);合并组年龄ȡ60岁㊁A S I A分级A级㊁术中失血量ȡ1000m L㊁输血㊁卧床时间ȡ4d的病人占比及术后D-二聚体水平均高于未合并组(P<0.05)㊂见表1㊂2.2胸腰椎骨折内固定术后合并下肢D V T影响因素的L o g i s t i c回归分析将表1中P<0.05的项目作为自变量(赋值见表2),将病人术后有无合并下肢D V T(未合并=0,合并=1)作为因变量进行多因素L o g i s t i c回归分析㊂结果显示,年龄ȡ60岁㊁A S I A分级A级㊁术中失血量ȡ1000 m L㊁输血㊁术后D-二聚体水平高㊁术后卧床时间ȡ4d 均是胸腰椎骨折内固定术后合并下肢D V T的危险因素(P<0.05)㊂见表3㊂㊃2093㊃C H I N E S EG E N E R A LP R A C T I C E N U R S I N G O c t o b e r2023V o l.21N o.28Copyright©博看网. All Rights Reserved.表1 合并组与未合并组临床资料比较 项目合并组(n =35)未合并组(n =190)统计值P 性别[例(%)] 男13(37.14)80(42.11)χ2=0.3000.584 女22(62.86)110(57.89) 年龄[例(%)] ȡ60岁21(60.00)78(41.05)χ2=4.3060.038 <60岁14(40.00)112(58.95) 体质指数(k g /m 2)24.56ʃ2.2623.89ʃ2.23t =1.6300.105高血压[例(%)] 是9(25.71)41(21.58)χ2=0.2920.589 否26(74.29)149(78.42) 糖尿病[例(%)] 是8(22.86)37(19.47)χ2=0.1410.707 否27(77.14)153(80.53) 冠心病[例(%)] 是2(5.71)6(3.61)χ2=0.2110.646 否33(94.29)184(96.84) A S I A 分级[例(%)] A 级11(31.43)①25(13.16) B 级6(17.14)43(22.63)χ2=4.2360.035C 级8(22.86)39(20.53)D 级10(28.57)83(43.68)损伤节段[例(%)] 胸126(17.14)36(18.95) 腰116(45.71)89(46.84)χ2=0.1330.712 腰28(22.86)40(21.05) 腰35(14.29)25(13.16)术中失血量[例(%)] ȡ1000m L18(51.43)55(28.95)χ2=6.8150.009 <1000m L17(48.57)135(71.05) 输血[例(%)] 是16(45.71)45(23.68)χ2=7.2590.007 否19(54.29)145(76.32) 手术时间[例(%)] ȡ2h30(85.71)133(70.00) χ2=3.6560.056<2h5(14.29)57(30.00)术后D -二聚体(m g/L )1.12ʃ0.350.56ʃ0.17t =14.652<0.001术后血小板计数(ˑ109/L )189.35ʃ38.23184.56ʃ37.63t =0.6900.491术后卧床时间[例(%)] ȡ4d20(57.14)63(33.16)χ2=7.3030.007 <4d15(42.86)127(66.84) 使用抗凝药物[例(%)] 是13(37.14)56(29.47)χ2=0.8180.366 否22(62.86)134(70.53)①与未合并组比较,P <0.01㊂表2 自变量赋值说明自变量 赋值方式年龄<60岁=0;ȡ60岁=1A S I A 分级D 级=0;C 级=1;B 级=2;A 级=3术中失血量<1000m L =0;ȡ1000m L =1输血否=0;是=1术后D -二聚体原值输入术后卧床时间<4d =0;ȡ4d =12.3 胸腰椎骨折内固定术后合并下肢D V T 预测列线图模型的构建㊁验证及其预测效能将多因素L o g i s t i c 回归分析筛选的6个危险因素作为预测指标,构建胸腰椎骨折内固定术后合并下肢D V T 的风险预测列线图模型,见图1㊂R O C 曲线分析结果显示,列线图预测胸腰椎骨折内固定术后合并下肢D V T 的曲线下面积(A U C )为0.797[95%C I (0.739,0.848)],灵敏度为74.29%,特异度为86.84%,见图2㊂采用B o o t s t r a p 自抽样法重复抽样1000次验证列线图模型的区分度,一致性指数为0.793,提示区分度良好㊂校准曲线图显示校准曲线与标准曲线贴合较好,见图3㊂表3 胸腰椎骨折内固定术后合并下肢D V T 影响因素的L o gi s t i c 回归分析 自变量回归系数标准误W a l d χ2值P O R 值95%C I年龄ȡ60岁0.5680.2515.1210.0271.765[1.012,3.191]A S I A 分级A 级比D 级0.8820.3795.4160.0252.416[1.348,3.725]A S I A 分级B 级比D 级0.2120.1262.8310.0781.236[0.973,1.363]A S I A 分级C 级比D 级0.1470.0922.5530.0961.158[0.995,1.289]术中失血量ȡ1000m L 1.5560.5827.1480.0074.740[3.425,5.612]输血1.3650.5256.7600.0103.916[2.929,4.956]术后D -二聚体水平高1.7480.6158.079<0.001 5.743[4.321,6.582]术后卧床时间ȡ4d 1.2630.5056.2550.0123.536[2.137,4.536]㊃3093㊃全科护理2023年10月第21卷第28期Copyright ©博看网. All Rights Reserved.图1胸腰椎骨折内固定术后合并下肢D V T 的预测列线图模型图2列线图模型预测胸腰椎骨折内固定术后合并下肢D V T的R O C 曲线图3列线图模型预测校准曲线图3讨论3.1胸腰椎骨折内固定术后下肢D V T发生率D V T是指深静脉内血液不正常凝结,可发生于全身主干静脉,以下肢D V T最为多见,是临床外科常见的术后并发症[9]㊂下肢D V T可引起下肢感觉和运动功能障碍,严重影响病人术后康复,甚至会发生血栓脱落随静脉血回流至肺脏而引发肺栓塞,导致病人死亡[10-11]㊂本研究结果显示,225例胸腰椎骨折病人术后合并下肢D V T的有35例,发生率为15.56%,稍高于易伟林等[12]研究报道的14.55%,但均表明胸腰椎骨折内固定术后合并下肢D V T的风险较高㊂因此,探讨胸腰椎骨折内固定术后合并下肢D V T的危险因素对预防术后病人下肢D V T发生,改善病人预后至关重要㊂3.2胸腰椎骨折内固定术后合并下肢D V T的危险因素本研究经多因素L o g i s t i c回归分析结果显示,年龄ȡ60岁㊁A S I A分级A级㊁术中失血量ȡ1000m L㊁输血㊁术后D-二聚体水平高㊁术后卧床时间ȡ4d均是胸腰椎骨折内固定术后合并下肢D V T的危险因素㊂Z h a n g等[13]研究报道,脊髓损伤病人年龄与D V T的发生率呈线性相关,本研究也发现60岁及以上病人术后合并下肢D V T的发生率是60岁以下病人的1.765倍,这可能与随着年龄增长,老年病人血管逐渐退化㊁内壁老化㊁内膜粗糙㊁损伤增多㊁产生促凝物质增多有关㊂A S I A分级A级病人脊髓完全损伤,基本处于瘫痪状态,严重影响交感神经对心脏供血功能的控制能力,使病人心肌收缩能力下降,下肢静脉血流供应不足,有效血容量下降,增加血液黏稠度;另外病人下肢感觉㊁肌张力逐渐丧失,使静脉血管失去支撑,血管内㊃4093㊃C H I N E S EG E N E R A LP R A C T I C E N U R S I N G O c t o b e r2023V o l.21N o.28Copyright©博看网. All Rights Reserved.径变窄,容易形成局部血流涡,增加下肢D V T的发生风险㊂J i a n g等[14]研究指出,A S I A分级是胸腰椎骨折伴脊髓损伤病人术后下肢D V T发生的危险因素,与本研究结果相符㊂胸腰椎骨折内固定术难度大㊁手术时间长㊁术中失血量多,病人往往需输血补充失血量,本研究中术中输血病人术后合并下肢D V T的概率是未输血病人的4倍㊂L i n等[15]研究发现,输血与D V T 的风险增加有关,可能是因为病人全血失血量过多,输入常规过滤红细胞悬液中含有血块及碎粒,增加了血液黏稠度,从而增加下肢D V T的发生风险[16]㊂D-二聚体是纤维蛋白的特殊衍生物,其水平升高说明病人血液处于高凝状态,是临床诊断下肢D V T的常用指标[17]㊂Y a m a s a k i等[18]研究发现,腰椎术后1周病人D-二聚体达到19.5μg/m L时,D V T的风险可增加4.09倍;W a n g等[19]研究也指出,D-二聚体是胸腰椎骨折病人术后D V T的危险因素㊂临床医护人员应密切关注病人D-二聚体水平,对于D-二聚体水平升高者应及时采取针对性措施防治下肢D V T㊂术后卧床时间ȡ4d 也是胸腰椎骨折病人术后合并下肢D V T的危险因素,长期卧床病人下肢肌肉泵功能减弱,使静脉血流缓慢或瘀滞,进一步导致下肢D V T发生㊂宋春利[20]研究发现,术后卧床时间ȡ4d的脊柱骨折病人术后下肢D V T的发生率是术后卧床时间<4d病人的3倍,与本研究结果相似㊂因此,胸腰椎骨折病人术后应尽早开展功能锻炼,减少卧床时间,或借助医院设备帮助病人增加下肢肌肉收缩力,促进血液回流,以预防或减少下肢D V T发生㊂3.3胸腰椎骨折内固定术后合并下肢D V T的列线图预测模型效能和临床价值近年来,列线图风险预测模型凭借其直观㊁准确的优点被广泛应用于医学领域,且取得了相对不错的预测效果[21]㊂本研究基于多因素L o g i s t i c回归分析筛选的年龄㊁A S I A分级㊁术后D-二聚体㊁术中失血量㊁输血㊁卧床时间等危险因素作为预测指标建立胸腰椎骨折内固定术后合并下肢D V T的风险预测列线图模型,该预测模型不仅特异度㊁灵敏度均较好,预测胸腰椎骨折内固定术后合并下肢D V T的风险与实际复发的风险相贴合,具有良好的准确性㊂临床医护人员通过收集病人临床资料,基于该列线图模型中危险因素进行评分,相加得到总分,向下在风险轴做垂线即可得出病人发生下肢D V T的概率,有利于临床医护人员方便㊁直观地评估胸腰椎骨折内固定术后病人下肢D V T的发生风险等级,进而给予个体化干预,降低术后病人合并下肢D V T的发生率㊂4总结综上所述,本研究基于年龄㊁A S I A分级㊁术中失血量㊁输血㊁术后D-二聚体㊁术后卧床时间等危险因素构建的胸腰椎骨折内固定术后合并下肢D V T的列线图风险预测模型,具有良好的一致性和区分度,对临床治疗方案及术后护理方案的制定具有一定的指导价值㊂本研究存在一定局限性,本研究为回顾性研究,数据的收集可能存在一定偏差,且本研究构建的风险预测列线图模型尚未通过临床大样本验证,这将需要在今后的研究中对该预测模型的应用价值进行进一步评价㊂参考文献:[1]耿明皓,孙建华,李晶,等.胸腰椎骨折复位内固定术后伤椎发生骨缺损的相关危险因素分析[J].中国脊柱脊髓杂志,2020,30(5): 410-416.[2] MA N U N G AJ,A L C A L AC,S M I T HJ,e t a l.T e c h n i c a l a p p r o a c h,o u t c o m e s,a n de x p o s u r e-r e l a t e dc o m p l i c a t i o n s i n p a t i e n t su n d e r g o i n ga n t e r i o r l u mb a r i n t e r b o d y f u s i o n[J].J o u r n a l o fV a sc u l a rS u r g e r y,2021,73(3):992-998.[3]邵露露,余文霞,唐青,等.胸腰椎骨折术后深静脉血栓的预防与护理[J].中国矫形外科杂志,2019,27(24):2295-2297.[4] T AMU R AS,Y AMAMO T O M,K I T A G AWA A,e t a l.D e e p v e i nt h r o m b o s i s(D V T)p r o p h y l a c t i ct e a m a c t i v i t y t os u p p o r t D V T p r e v e n t i o n p r o t o c o l f o r t h e p u r p o s e o f t h e p r o p h y l a x i s o f p u l m o n a r y t h r o m b o e m b o l i s m(P T E)a n do p e r a t i o n[J].A n n a l so f V a s c u l a rD i s e a s e s,2021,14(2):99-107.[5]刘晓宇,王恬,单亚维,等.髋部骨折术后谵妄风险预测模型的研究进展[J].中华现代护理杂志,2021,27(13):1808-1811. 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[18] Y AMA S A K IK,H O S H I N O M,OMO R IK,e t a l.P r e v a l e n c ea n dr i s k f a c t o r so fd e e p v e i nt h r o m b o s i s i n p a t i e n t su n d e r g o i n g l u m b a r s p i n e s u r g e r y[J].J o u r n a lo fO r t h o p a e d i cS c i e n c e,2017,22(6): 1021-1025.[19] WA N G H Y,P E I H L,D I N G W Y,e t a l.R i s k f a c t o r s o fp o s t o p e r a t i v e d e e p v e i nt h r o m b o s i s(D V T)u n d e r l o w m o l e c u l a rw e i g h t h e p a r i n(L MWH)p r o p h y l a x i s i n p a t i e n t sw i t h t h o r a c o l u m b a rf r a c t u r e s c a u s e db y h ig h-e n e r g y i n j u r i e s[J].J o u r n a lo fTh r o m b o si sa n dT h r o mb o l y s i s,2021,51(2):397-404.[20]宋春利.脊柱骨折术后下肢D V T危险因素及针对性预防护理对策探讨[J].中华现代护理杂志,2020,26(25):3534-3538.[21] Z H A N GL,H E M,J I A W L,e ta l.A n a l y s i so fh i g h-r i s kf a c t o r sf o r p r e o p e r a t i v e D V T i n e l d e r l y p a t i e n t s w i t h s i m p l e h i p f r a c t u r e s a n dc o n s t r u c t i o n o f a n o m o g r a m p r ed i c t i o n m o de l[J].B M CM u s c u l o s k e l e t a lD i s o r d e r s,2022,23(1):441.(收稿日期:2022-12-20;修回日期:2023-10-07)(本文编辑李进鹏)㊃5093㊃全科护理2023年10月第21卷第28期Copyright©博看网. All Rights Reserved.。